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Journal articles on the topic 'Maximum Mean Discrepancy (MMD)'

Author: Grafiati

Published: 6 September 2023

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1

Huang, Qihang, Yulin He, and Zhexue Huang. "A Novel Maximum Mean Discrepancy-Based Semi-Supervised Learning Algorithm." Mathematics 10, no. 1 (2021): 39. http://dx.doi.org/10.3390/math10010039.

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To provide more external knowledge for training self-supervised learning (SSL) algorithms, this paper proposes a maximum mean discrepancy-based SSL (MMD-SSL) algorithm, which trains a well-performing classifier by iteratively refining the classifier using highly confident unlabeled samples. The MMD-SSL algorithm performs three main steps. First, a multilayer perceptron (MLP) is trained based on the labeled samples and is then used to assign labels to unlabeled samples. Second, the unlabeled samples are divided into multiple groups with the k-means clustering algorithm. Third, the maximum mean

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2

Zhou, Zhaokun, Yuanhong Zhong, Xiaoming Liu, Qiang Li, and Shu Han. "DC-MMD-GAN: A New Maximum Mean Discrepancy Generative Adversarial Network Using Divide and Conquer." Applied Sciences 10, no. 18 (2020): 6405. http://dx.doi.org/10.3390/app10186405.

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Generative adversarial networks (GANs) have a revolutionary influence on sample generation. Maximum mean discrepancy GANs (MMD-GANs) own competitive performance when compared with other GANs. However, the loss function of MMD-GANs is an empirical estimate of maximum mean discrepancy (MMD) and not precise in measuring the distance between sample distributions, which inhibits MMD-GANs training. We propose an efficient divide-and-conquer model, called DC-MMD-GANs, which constrains the loss function of MMD to tight bound on the deviation between empirical estimate and expected value of MMD and acc

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3

Xu, Haoji. "Generate Faces Using Ladder Variational Autoencoder with Maximum Mean Discrepancy (MMD)." Intelligent Information Management 10, no. 04 (2018): 108–13. http://dx.doi.org/10.4236/iim.2018.104009.

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4

Sun, Jiancheng. "Complex Network Construction of Univariate Chaotic Time Series Based on Maximum Mean Discrepancy." Entropy 22, no. 2 (2020): 142. http://dx.doi.org/10.3390/e22020142.

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The analysis of chaotic time series is usually a challenging task due to its complexity. In this communication, a method of complex network construction is proposed for univariate chaotic time series, which provides a novel way to analyze time series. In the process of complex network construction, how to measure the similarity between the time series is a key problem to be solved. Due to the complexity of chaotic systems, the common metrics is hard to measure the similarity. Consequently, the proposed method first transforms univariate time series into high-dimensional phase space to increase

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Zhang, Xiangqing, Yan Feng, Shun Zhang, Nan Wang, Shaohui Mei, and Mingyi He. "Semi-Supervised Person Detection in Aerial Images with Instance Segmentation and Maximum Mean Discrepancy Distance." Remote Sensing 15, no. 11 (2023): 2928. http://dx.doi.org/10.3390/rs15112928.

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Detecting sparse, small, lost persons with only a few pixels in high-resolution aerial images was, is, and remains an important and difficult mission, in which a vital role is played by accurate monitoring and intelligent co-rescuing for the search and rescue (SaR) system. However, many problems have not been effectively solved in existing remote-vision-based SaR systems, such as the shortage of person samples in SaR scenarios and the low tolerance of small objects for bounding boxes. To address these issues, a copy-paste mechanism (ISCP) with semi-supervised object detection (SSOD) via instan

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Zhao, Ji, and Deyu Meng. "FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test." Neural Computation 27, no. 6 (2015): 1345–72. http://dx.doi.org/10.1162/neco_a_00732.

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The maximum mean discrepancy (MMD) is a recently proposed test statistic for the two-sample test. Its quadratic time complexity, however, greatly hampers its availability to large-scale applications. To accelerate the MMD calculation, in this study we propose an efficient method called FastMMD. The core idea of FastMMD is to equivalently transform the MMD with shift-invariant kernels into the amplitude expectation of a linear combination of sinusoid components based on Bochner’s theorem and Fourier transform (Rahimi & Recht, 2007 ). Taking advantage of sampling the Fourier transform, FastM

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Williamson, Sinead A., and Jette Henderson. "Understanding Collections of Related Datasets Using Dependent MMD Coresets." Information 12, no. 10 (2021): 392. http://dx.doi.org/10.3390/info12100392.

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Understanding how two datasets differ can help us determine whether one dataset under-represents certain sub-populations, and provides insights into how well models will generalize across datasets. Representative points selected by a maximum mean discrepancy (MMD) coreset can provide interpretable summaries of a single dataset, but are not easily compared across datasets. In this paper, we introduce dependent MMD coresets, a data summarization method for collections of datasets that facilitates comparison of distributions. We show that dependent MMD coresets are useful for understanding multip

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Li, Kangji, Borui Wei, Qianqian Tang, and Yufei Liu. "A Data-Efficient Building Electricity Load Forecasting Method Based on Maximum Mean Discrepancy and Improved TrAdaBoost Algorithm." Energies 15, no. 23 (2022): 8780. http://dx.doi.org/10.3390/en15238780.

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Building electricity load forecasting plays an important role in building energy management, peak demand and power grid security. In the past two decades, a large number of data-driven models have been applied to building and larger-scale energy consumption predictions. Although these models have been successful in specific cases, their performances would be greatly affected by the quantity and quality of the building data. Moreover, for older buildings with sparse data, or new buildings with no historical data, accurate predictions are difficult to achieve. Aiming at such a data silos problem

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Lee, Junghyun, Gwangsu Kim, Mahbod Olfat, Mark Hasegawa-Johnson, and Chang D. Yoo. "Fast and Efficient MMD-Based Fair PCA via Optimization over Stiefel Manifold." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7363–71. http://dx.doi.org/10.1609/aaai.v36i7.20699.

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This paper defines fair principal component analysis (PCA) as minimizing the maximum mean discrepancy (MMD) between the dimensionality-reduced conditional distributions of different protected classes. The incorporation of MMD naturally leads to an exact and tractable mathematical formulation of fairness with good statistical properties. We formulate the problem of fair PCA subject to MMD constraints as a non-convex optimization over the Stiefel manifold and solve it using the Riemannian Exact Penalty Method with Smoothing (REPMS). Importantly, we provide a local optimality guarantee and explic

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Han, Chao, Deyun Zhou, Zhen Yang, Yu Xie, and Kai Zhang. "Discriminative Sparse Filtering for Multi-Source Image Classification." Sensors 20, no. 20 (2020): 5868. http://dx.doi.org/10.3390/s20205868.

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Distribution mismatch caused by various resolutions, backgrounds, etc. can be easily found in multi-sensor systems. Domain adaptation attempts to reduce such domain discrepancy by means of different measurements, e.g., maximum mean discrepancy (MMD). Despite their success, such methods often fail to guarantee the separability of learned representation. To tackle this issue, we put forward a novel approach to jointly learn both domain-shared and discriminative representations. Specifically, we model the feature discrimination explicitly for two domains. Alternating discriminant optimization is

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Song, Mengmeng, Zexiong Zhang, Shungen Xiao, Zicheng Xiong, and Mengwei Li. "Bearing fault diagnosis method using a spatio-temporal neural network based on feature transfer learning." Measurement Science and Technology 34, no. 1 (2022): 015119. http://dx.doi.org/10.1088/1361-6501/ac9078.

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Abstract An intelligent bearing fault diagnosis method based requires a large quantity of labeled data. However, in an actual engineering environment, only a tiny amount of unlabeled data can be collected. To solve this problem, we construct a spatio-temporal neural network (STN) model by multi-layer fusion of convolutional neural network (CNN) and long-term memory network features. Then, a model based on feature migration is constructed and a STN is applied as the feature extractor of the network. Finally, the Case Western Reserve University bearing dataset is employed to verify the performan

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Wang, Jinrui, Shanshan Ji, Baokun Han, Huaiqian Bao, and Xingxing Jiang. "Deep Adaptive Adversarial Network-Based Method for Mechanical Fault Diagnosis under Different Working Conditions." Complexity 2020 (July 23, 2020): 1–11. http://dx.doi.org/10.1155/2020/6946702.

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The demand for transfer learning methods for mechanical fault diagnosis has considerably progressed in recent years. However, the existing methods always depend on the maximum mean discrepancy (MMD) in measuring the domain discrepancy. But MMD can not guarantee the different domain features to be similar enough. Inspired by generative adversarial networks (GAN) and domain adversarial training of neural networks (DANN), this study presents a novel deep adaptive adversarial network (DAAN). The DAAN comprises a condition recognition module and domain adversarial learning module. The condition rec

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Wang, Haoyu, Yuhu Cheng, and Xuesong Wang. "A Novel Hyperspectral Image Classification Method Using Class-Weighted Domain Adaptation Network." Remote Sensing 15, no. 4 (2023): 999. http://dx.doi.org/10.3390/rs15040999.

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With the development of science and technology, hyperspectral image (HSI) classification has been studied in depth by researchers as one of the important means of human cognition in living environments and the exploration of surface information. Nevertheless, the shortage of labeled samples is a major difficulty in HSI classification. To address this issue, we propose a novel HSI classification method called class-weighted domain adaptation network (CWDAN). First, the convolutional domain adaption network (ConDAN) is designed to align the marginal distributions and second-order statistics, res

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Liu, Yi, Hang Xiang, Zhansi Jiang, and Jiawei Xiang. "A Domain Adaption ResNet Model to Detect Faults in Roller Bearings Using Vibro-Acoustic Data." Sensors 23, no. 6 (2023): 3068. http://dx.doi.org/10.3390/s23063068.

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Intelligent fault diagnosis of roller bearings is facing two important problems, one is that train and test datasets have the same distribution, and the other is the installation positions of accelerometer sensors are limited in industrial environments, and the collected signals are often polluted by background noise. In the recent years, the discrepancy between train and test datasets is decreased by introducing the idea of transfer learning to solve the first issue. In addition, the non-contact sensors will replace the contact sensors. In this paper, a domain adaption residual neural network

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Xiao, Li, Qi Chen, Shuping Hou, Zhi Yan, and Yiming Tian. "Detection of an Incipient Fault for Dual Three-Phase PMSMs Using a Modified Autoencoder." Electronics 11, no. 22 (2022): 3741. http://dx.doi.org/10.3390/electronics11223741.

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For the detection of incipient interturn short-circuit (IITSC) faults of machines without shutting them down, there are still shortcomings of insufficient incipient fault features and a high false alarm rate. This is especially the case for dual three-phase permanent magnet synchronous motors (PMSMs) with complex winding structures, and this kind of incipient fault detection is more complicated. To solve this detection difficulty, an IITSC detection method for dual three-phase PMSMs is proposed based on a modified deep autoencoder (MDAE). This autoencoder (AE) adopts an improved distribution m

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Futami, Futoshi, Zhenghang Cui, Issei Sato, and Masashi Sugiyama. "Bayesian Posterior Approximation via Greedy Particle Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3606–13. http://dx.doi.org/10.1609/aaai.v33i01.33013606.

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In Bayesian inference, the posterior distributions are difficult to obtain analytically for complex models such as neural networks. Variational inference usually uses a parametric distribution for approximation, from which we can easily draw samples. Recently discrete approximation by particles has attracted attention because of its high expression ability. An example is Stein variational gradient descent (SVGD), which iteratively optimizes particles. Although SVGD has been shown to be computationally efficient empirically, its theoretical properties have not been clarified yet and no finite s

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Du, Yuntao, Ruiting Zhang, Xiaowen Zhang, Yirong Yao, Hengyang Lu, and Chongjun Wang. "Learning transferable and discriminative features for unsupervised domain adaptation." Intelligent Data Analysis 26, no. 2 (2022): 407–25. http://dx.doi.org/10.3233/ida-215813.

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Although achieving remarkable progress, it is very difficult to induce a supervised classifier without any labeled data. Unsupervised domain adaptation is able to overcome this challenge by transferring knowledge from a labeled source domain to an unlabeled target domain. Transferability and discriminability are two key criteria for characterizing the superiority of feature representations to enable successful domain adaptation. In this paper, a novel method called learning TransFerable and Discriminative Features for unsupervised domain adaptation (TFDF) is proposed to optimize these two obje

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Wang, Z., T. Li, L. Pan, and Z. Kang. "SCENE SEMANTIC SEGMENTATION FROM INDOOR RGB-D IMAGES USING ENCODE-DECODER FULLY CONVOLUTIONAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 397–404. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-397-2017.

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With increasing attention for the indoor environment and the development of low-cost RGB-D sensors, indoor RGB-D images are easily acquired. However, scene semantic segmentation is still an open area, which restricts indoor applications. The depth information can help to distinguish the regions which are difficult to be segmented out from the RGB images with similar color or texture in the indoor scenes. How to utilize the depth information is the key problem of semantic segmentation for RGB-D images. In this paper, we propose an Encode-Decoder Fully Convolutional Networks for RGB-D image clas

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Cheng, Xiuyuan, Alexander Cloninger, and Ronald R. Coifman. "Two-sample statistics based on anisotropic kernels." Information and Inference: A Journal of the IMA 9, no. 3 (2019): 677–719. http://dx.doi.org/10.1093/imaiai/iaz018.

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Abstract The paper introduces a new kernel-based Maximum Mean Discrepancy (MMD) statistic for measuring the distance between two distributions given finitely many multivariate samples. When the distributions are locally low-dimensional, the proposed test can be made more powerful to distinguish certain alternatives by incorporating local covariance matrices and constructing an anisotropic kernel. The kernel matrix is asymmetric; it computes the affinity between $n$ data points and a set of $n_R$ reference points, where $n_R$ can be drastically smaller than $n$. While the proposed statistic can

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Chen, Chao, Zhihang Fu, Zhihong Chen, et al. "HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3422–29. http://dx.doi.org/10.1609/aaai.v34i04.5745.

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Minimizing the discrepancy of feature distributions between different domains is one of the most promising directions in unsupervised domain adaptation. From the perspective of moment matching, most existing discrepancy-based methods are designed to match the second-order or lower moments, which however, have limited expression of statistical characteristic for non-Gaussian distributions. In this work, we propose a Higher-order Moment Matching (HoMM) method, and further extend the HoMM into reproducing kernel Hilbert spaces (RKHS). In particular, our proposed HoMM can perform arbitrary-order m

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Liu, Jian, and Liming Feng. "Diversity Evolutionary Policy Deep Reinforcement Learning." Computational Intelligence and Neuroscience 2021 (August 3, 2021): 1–11. http://dx.doi.org/10.1155/2021/5300189.

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The reinforcement learning algorithms based on policy gradient may fall into local optimal due to gradient disappearance during the update process, which in turn affects the exploration ability of the reinforcement learning agent. In order to solve the above problem, in this paper, the cross-entropy method (CEM) in evolution policy, maximum mean difference (MMD), and twin delayed deep deterministic policy gradient algorithm (TD3) are combined to propose a diversity evolutionary policy deep reinforcement learning (DEPRL) algorithm. By using the maximum mean discrepancy as a measure of the dista

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Tahmoresnezhad, Jafar, and Sattar Hashemi. "An Efficient yet Effective Random Partitioning and Feature Weighting Approach for Transfer Learning." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 02 (2016): 1651003. http://dx.doi.org/10.1142/s0218001416510034.

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One of the serious challenges in machine learning and pattern recognition is to transfer knowledge from related but different domains to a new unlabeled domain. Feature selection with maximum mean discrepancy (f-MMD) is a novel and effective approach to transfer knowledge from source domain (training set) into target domain (test set) where training and test sets are drawn from different distributions. However, f-MMD has serious challenges in facing datasets with large number of samples and features. Moreover, f-MMD ignores the feature-label relation in finding the reduced representation of da

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Tay, Sebastian Shenghong, Xinyi Xu, Chuan Sheng Foo, and Bryan Kian Hsiang Low. "Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 9448–56. http://dx.doi.org/10.1609/aaai.v36i9.21177.

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This paper presents a novel collaborative generative modeling (CGM) framework that incentivizes collaboration among self-interested parties to contribute data to a pool for training a generative model (e.g., GAN), from which synthetic data are drawn and distributed to the parties as rewards commensurate to their contributions. Distributing synthetic data as rewards (instead of trained models or money) offers task- and model-agnostic benefits for downstream learning tasks and is less likely to violate data privacy regulation. To realize the framework, we firstly propose a data valuation functio

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Zhang, Quanling, Ningze Tang, Xing Fu, Hao Peng, Cuimei Bo, and Cunsong Wang. "A Multi-Scale Attention Mechanism Based Domain Adversarial Neural Network Strategy for Bearing Fault Diagnosis." Actuators 12, no. 5 (2023): 188. http://dx.doi.org/10.3390/act12050188.

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There are a large number of bearings in aircraft engines that are subjected to extreme operating conditions, such as high temperature, high speed, and heavy load, and their fatigue, wear, and other failure problems seriously affect the reliability of the engine. The complex and variable bearing operating conditions can lead to differences in the distribution of data between the source and target operating conditions, as well as insufficient labels. To solve the above challenges, a multi-scale attention mechanism-based domain adversarial neural network strategy for bearing fault diagnosis (MADA

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Xu, Kun, Shunming Li, Ranran Li, Jiantao Lu, Xianglian Li, and Mengjie Zeng. "Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis." Applied Sciences 11, no. 17 (2021): 7983. http://dx.doi.org/10.3390/app11177983.

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Due to the mechanical equipment working under variable speed and load for a long time, the distribution of samples is different (domain shift). The general intelligent fault diagnosis method has a good diagnostic effect only on samples with the same sample distribution, but cannot correctly predict the faults of samples with domain shift in a real situation. To settle this problem, a new intelligent fault diagnosis method, domain adaptation network with double adversarial mechanism (DAN-DAM), is proposed. The DAN-DAM model is mainly composed of a feature extractor, two label classifiers and a

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Wang, Li, Guoqiang Liu, Chao Zhang, Yu Yang, and Jinhao Qiu. "FEM Simulation-Based Adversarial Domain Adaptation for Fatigue Crack Detection Using Lamb Wave." Sensors 23, no. 4 (2023): 1943. http://dx.doi.org/10.3390/s23041943.

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Lamb wave-based damage detection technology shows great potential for structural integrity assessment. However, conventional damage features based damage detection methods and data-driven intelligent damage detection methods highly rely on expert knowledge and sufficient labeled data for training, for which collecting is usually expensive and time-consuming. Therefore, this paper proposes an automated fatigue crack detection method using Lamb wave based on finite element method (FEM) and adversarial domain adaptation. FEM-simulation was used to obtain simulated response signals under various c

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Yang, Bingru, Qi Li, Liang Chen, Changqing Shen, and Sundararajan Natarajan. "Bearing Fault Diagnosis Based on Multilayer Domain Adaptation." Shock and Vibration 2020 (September 29, 2020): 1–11. http://dx.doi.org/10.1155/2020/8873960.

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Bearing fault diagnosis plays a vitally important role in practical industrial scenarios. Deep learning-based fault diagnosis methods are usually performed on the hypothesis that the training set and test set obey the same probability distribution, which is hard to satisfy under the actual working conditions. This paper proposes a novel multilayer domain adaptation (MLDA) method, which can diagnose the compound fault and single fault of multiple sizes simultaneously. A special designed residual network for the fault diagnosis task is pretrained to extract domain-invariant features. The multike

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Banerjee, Subhankar, and Shayok Chakraborty. "Deterministic Mini-batch Sequencing for Training Deep Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 6723–31. http://dx.doi.org/10.1609/aaai.v35i8.16831.

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Recent advancements in the field of deep learning have dramatically improved the performance of machine learning models in a variety of applications, including computer vision, text mining, speech processing and fraud detection among others. Mini-batch gradient descent is the standard algorithm to train deep models, where mini-batches of a fixed size are sampled randomly from the training data and passed through the network sequentially. In this paper, we present a novel algorithm to generate a deterministic sequence of mini-batches to train a deep neural network (rather than a random sequence

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Tian, Jinghui, Dongying Han, Lifeng Xiao, and Peiming Shi. "Multi-scale deep coupling convolutional neural network with heterogeneous sensor data for intelligent fault diagnosis." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 2225–38. http://dx.doi.org/10.3233/jifs-210932.

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With the innovation and development of detection technology, various types of sensors are installed to monitor the operating status of equipment in modern industry. Compared with the same type of sensors for monitoring, heterogeneous sensors can collect more comprehensive complementary fault information. Due to the large distribution differences and serious noise pollution of heterogeneous sensor data collected in industrial sites, this brings certain challenges to the development of heterogeneous data fusion strategies. In view of the large distribution difference in the feature spatial of he

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Zang, Shaofei, Xinghai Li, Jianwei Ma, Yongyi Yan, Jiwei Gao, and Yuan Wei. "TSTELM: Two-Stage Transfer Extreme Learning Machine for Unsupervised Domain Adaptation." Computational Intelligence and Neuroscience 2022 (July 18, 2022): 1–18. http://dx.doi.org/10.1155/2022/1582624.

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As a single-layer feedforward network (SLFN), extreme learning machine (ELM) has been successfully applied for classification and regression in machine learning due to its faster training speed and better generalization. However, it will perform poorly for domain adaptation in which the distributions between training data and testing data are inconsistent. In this article, we propose a novel ELM called two-stage transfer extreme learning machine (TSTELM) to solve this problem. At the statistical matching stage, we adopt maximum mean discrepancy (MMD) to narrow the distribution difference of th

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Sun, Wei, Jie Zhou, Bintao Sun, Yuqing Zhou, and Yongying Jiang. "Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring." Micromachines 13, no. 6 (2022): 873. http://dx.doi.org/10.3390/mi13060873.

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Tool condition monitoring (TCM) is of great importance for improving the manufacturing efficiency and surface quality of workpieces. Data-driven machine learning methods are widely used in TCM and have achieved many good results. However, in actual industrial scenes, labeled data are not available in time in the target domain that significantly affect the performance of data-driven methods. To overcome this problem, a new TCM method combining the Markov transition field (MTF) and the deep domain adaptation network (DDAN) is proposed. A few vibration signals collected in the TCM experiments wer

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Ding, Renjie, Xue Li, Lanshun Nie, et al. "Empirical Study and Improvement on Deep Transfer Learning for Human Activity Recognition." Sensors 19, no. 1 (2018): 57. http://dx.doi.org/10.3390/s19010057.

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Human activity recognition (HAR) based on sensor data is a significant problem in pervasive computing. In recent years, deep learning has become the dominating approach in this field, due to its high accuracy. However, it is difficult to make accurate identification for the activities of one individual using a model trained on data from other users. The decline on the accuracy of recognition restricts activity recognition in practice. At present, there is little research on the transferring of deep learning model in this field. This is the first time as we known, an empirical study was carried

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Wang, Kai, Wei Zhao, Aidong Xu, Peng Zeng, and Shunkun Yang. "One-Dimensional Multi-Scale Domain Adaptive Network for Bearing-Fault Diagnosis under Varying Working Conditions." Sensors 20, no. 21 (2020): 6039. http://dx.doi.org/10.3390/s20216039.

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Data-driven bearing-fault diagnosis methods have become a research hotspot recently. These methods have to meet two premises: (1) the distributions of the data to be tested and the training data are the same; (2) there are a large number of high-quality labeled data. However, machines usually work under different working conditions in practice, which challenges these prerequisites due to the fact that the data distributions under different working conditions are different. In this paper, the one-dimensional Multi-Scale Domain Adaptive Network (1D-MSDAN) is proposed to address this issue. The 1

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Park, Hyo-Seok, Seong-Joong Kim, Andrew L. Stewart, Seok-Woo Son, and Kyong-Hwan Seo. "Mid-Holocene Northern Hemisphere warming driven by Arctic amplification." Science Advances 5, no. 12 (2019): eaax8203. http://dx.doi.org/10.1126/sciadv.aax8203.

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The Holocene thermal maximum was characterized by strong summer solar heating that substantially increased the summertime temperature relative to preindustrial climate. However, the summer warming was compensated by weaker winter insolation, and the annual mean temperature of the Holocene thermal maximum remains ambiguous. Using multimodel mid-Holocene simulations, we show that the annual mean Northern Hemisphere temperature is strongly correlated with the degree of Arctic amplification and sea ice loss. Additional model experiments show that the summer Arctic sea ice loss persists into winter

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Sun, Han, Xinyi Chen, Ling Wang, Dong Liang, Ningzhong Liu, and Huiyu Zhou. "C2DAN: An Improved Deep Adaptation Network with Domain Confusion and Classifier Adaptation." Sensors 20, no. 12 (2020): 3606. http://dx.doi.org/10.3390/s20123606.

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Deep neural networks have been successfully applied in domain adaptation which uses the labeled data of source domain to supplement useful information for target domain. Deep Adaptation Network (DAN) is one of these efficient frameworks, it utilizes Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to align the feature distribution in a reproducing kernel Hilbert space. However, DAN does not perform very well in feature level transfer, and the assumption that source and target domain share classifiers is too strict in different adaptation scenarios. In this paper, we further improve the adaptabil

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Zhang, Yongchao, Zhaohui Ren, and Shihua Zhou. "A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions." Shock and Vibration 2020 (July 24, 2020): 1–14. http://dx.doi.org/10.1155/2020/8850976.

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Effective fault diagnosis methods can ensure the safe and reliable operation of the machines. In recent years, deep learning technology has been applied to diagnose various mechanical equipment faults. However, in real industries, the data distribution under different working conditions is often different, which leads to serious degradation of diagnostic performance. In order to solve the issue, this study proposes a new deep convolutional domain adaptation network (DCDAN) method for bearing fault diagnosis. This method implements cross-domain fault diagnosis by using the labeled source domain

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Zhang, Jun, Wen Yao, Xiaoqian Chen, and Ling Feng. "Transferable Post-hoc Calibration on Pretrained Transformers in Noisy Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13940–48. http://dx.doi.org/10.1609/aaai.v37i11.26632.

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Recent work has demonstrated that pretrained transformers are overconfident in text classification tasks, which can be calibrated by the famous post-hoc calibration method temperature scaling (TS). Character or word spelling mistakes are frequently encountered in real applications and greatly threaten transformer model safety. Research on calibration under noisy settings is rare, and we focus on this direction. Based on a toy experiment, we discover that TS performs poorly when the datasets are perturbed by slight noise, such as swapping the characters, which results in distribution shift. We

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Paul, A., K. Vogt, F. Rottensteiner, J. Ostermann, and C. Heipke. "A COMPARISON OF TWO STRATEGIES FOR AVOIDING NEGATIVE TRANSFER IN DOMAIN ADAPTATION BASED ON LOGISTIC REGRESSION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 845–52. http://dx.doi.org/10.5194/isprs-archives-xlii-2-845-2018.

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In this paper we deal with the problem of measuring the similarity between training and tests datasets in the context of transfer learning (TL) for image classification. TL tries to transfer knowledge from a source domain, where labelled training samples are abundant but the data may follow a different distribution, to a target domain, where labelled training samples are scarce or even unavailable, assuming that the domains are related. Thus, the requirements w.r.t. the availability of labelled training samples in the target domain are reduced. In particular, if no labelled target data are ava

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Li, Zhaokui, Xiangyi Tang, Wei Li, Chuanyun Wang, Cuiwei Liu, and Jinrong He. "A Two-stage Deep Domain Adaptation Method for Hyperspectral Image Classification." Remote Sensing 12, no. 7 (2020): 1054. http://dx.doi.org/10.3390/rs12071054.

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Deep learning has attracted extensive attention in the field of hyperspectral images (HSIs) classification. However, supervised deep learning methods heavily rely on a large amount of label information. To address this problem, in this paper, we propose a two-stage deep domain adaptation method for hyperspectral image classification, which can minimize the data shift between two domains and learn a more discriminative deep embedding space with very few labeled target samples. A deep embedding space is first learned by minimizing the distance between the source domain and the target domain base

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Chen, Zhihong, Taiping Yao, Kekai Sheng, et al. "Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1132–39. http://dx.doi.org/10.1609/aaai.v35i2.16199.

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Face anti-spoofing approach based on domain generalization (DG) has drawn growing attention due to its robustness for unseen scenarios. Existing DG methods assume that the domain label is known. However, in real-world applications, the collected dataset always contains mixture domains, where the domain label is unknown. In this case, most of existing methods may not work. Further, even if we can obtain the domain label as existing methods, we think this is just a sub-optimal partition. To overcome the limitation, we propose domain dynamic adjustment meta-learning (D$^2$AM) without using domain

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Nguyen-Tang, Thanh, Sunil Gupta, and Svetha Venkatesh. "Distributional Reinforcement Learning via Moment Matching." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 9144–52. http://dx.doi.org/10.1609/aaai.v35i10.17104.

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We consider the problem of learning a set of probability distributions from the empirical Bellman dynamics in distributional reinforcement learning (RL), a class of state-of-the-art methods that estimate the distribution, as opposed to only the expectation, of the total return. We formulate a method that learns a finite set of statistics from each return distribution via neural networks, as in the distributional RL literature. Existing distributional RL methods however constrain the learned statistics to predefined functional forms of the return distribution which is both restrictive in repres

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Zhang, Qiyang, Zhibin Zhao, Xingwu Zhang, Yilong Liu, Xiaolei Yu, and Xuefeng Chen. "Short-time consistent domain adaptation for rolling bearing fault diagnosis under varying working conditions." Measurement Science and Technology 33, no. 7 (2022): 075105. http://dx.doi.org/10.1088/1361-6501/ac5874.

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Abstract Although traditional deep learning improves the accuracy of intelligent fault diagnosis, it suffers from a problem, which is that a change in working conditions may reduce the diagnostic accuracy. The reason for this phenomenon is that a change of working conditions influences the probability distributions. To solve this problem, domain adaptation is adopted to perform intelligent fault diagnosis. However, the design of regularization methods, such as maximum mean discrepancy (MMD), neglects the phenomenon of fault extension. Considering the property of fault extension, the paper sums

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Hussein, Amir, and Hazem Hajj. "Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series." ACM Transactions on Internet of Things 3, no. 2 (2022): 1–26. http://dx.doi.org/10.1145/3502905.

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In many real-world scenarios, machine learning models fall short in prediction performance due to data characteristics changing from training on one source domain to testing on a target domain. There has been extensive research to address this problem with Domain Adaptation (DA) for learning domain invariant features. However, when considering advances for time series, those methods remain limited to the use of hard parameter sharing (HPS) between source and target models, and the use of domain adaptation objective function. To address these challenges, we propose a soft parameter sharing (SPS

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He, Yiwei, Yingjie Tian, Jingjing Tang, and Yue Ma. "Unsupervised Domain Adaptation Using Exemplar-SVMs with Adaptation Regularization." Complexity 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/8425821.

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Domain adaptation has recently attracted attention for visual recognition. It assumes that source and target domain data are drawn from the same feature space but different margin distributions and its motivation is to utilize the source domain instances to assist in training a robust classifier for target domain tasks. Previous studies always focus on reducing the distribution mismatch across domains. However, in many real-world applications, there also exist problems of sample selection bias among instances in a domain; this would reduce the generalization performance of learners. To address

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Zhu, Qiuyu, Liheng Hu, and Rui Wang. "Image Clustering Algorithm Based on Predefined Evenly-Distributed Class Centroids and Composite Cosine Distance." Entropy 24, no. 11 (2022): 1533. http://dx.doi.org/10.3390/e24111533.

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The clustering algorithms based on deep neural network perform clustering by obtaining the optimal feature representation. However, in the face of complex natural images, the cluster accuracy of existing clustering algorithms is still relatively low. This paper presents an image clustering algorithm based on predefined evenly-distributed class centroids (PEDCC) and composite cosine distance. Compared with the current popular auto-encoder structure, we design an encoder-only network structure with normalized latent features, and two effective loss functions in latent feature space by replacing

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Ye, Fei, and Adrian G. Bors. "Lifelong Compression Mixture Model via Knowledge Relationship Graph." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10900–10908. http://dx.doi.org/10.1609/aaai.v37i9.26292.

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Task-Free Continual Learning (TFCL) represents a challenging scenario for lifelong learning because the model, under this paradigm, does not access any task information. The Dynamic Expansion Model (DEM) has shown promising results in this scenario due to its scalability and generalisation power. However, DEM focuses only on addressing forgetting and ignores minimizing the model size, which limits its deployment in practical systems. In this work, we aim to simultaneously address network forgetting and model size optimization by developing the Lifelong Compression Mixture Model (LGMM) equipped

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Zhang, Yizong, Shaobo Li, Qiuchen He, Ansi Zhang, Chuanjiang Li, and Zihao Liao. "An Intelligent Fault Detection Framework for FW-UAV Based on Hybrid Deep Domain Adaptation Networks and the Hampel Filter." International Journal of Intelligent Systems 2023 (June 7, 2023): 1–19. http://dx.doi.org/10.1155/2023/6608967.

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Fixed-wing unmanned aerial vehicles (FW-UAVs) play an essential role in many fields, but the faults of FW-UAV components lead to severe accidents frequently; so, there is a need to continuously explore more intelligent fault detection methods to improve the safety and reliability of FW-UAVs. Deep learning provides advanced solution ideas for future UAV fault detection, but the current lack of UAV monitoring data limits the advantages of deep learning in UAV fault detection, which are both a challenge and an opportunity. In this paper, we mainly consider the data availability of deep learning u

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Li, Xianling, Kai Zhang, Weijun Li, Yi Feng, and Ruonan Liu. "A Two-Stage Transfer Regression Convolutional Neural Network for Bearing Remaining Useful Life Prediction." Machines 10, no. 5 (2022): 369. http://dx.doi.org/10.3390/machines10050369.

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Recently, deep learning techniques have been successfully used for bearing remaining useful life (RUL) prediction. However, the degradation pattern of bearings can be much different from each other, which leads to the trained model usually not being able to work well for RUL prediction of a new bearing. As a method that can adapt a model trained on source datasets to a different but relative unlabeled target dataset, transfer learning shows the potential to solve this problem. Therefore, we propose a two-stage transfer regression (TR)-based bearing RUL prediction method. Firstly, the incipient

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Tong, Zhe, Wei Li, Bo Zhang, and Meng Zhang. "Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions." Shock and Vibration 2018 (June 28, 2018): 1–12. http://dx.doi.org/10.1155/2018/6714520.

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Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy-dropping problem of fault diagnosis. Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge. In this paper, a novel bearing fault diagnosis under different working conditions

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Ayalew, Melese, Shijie Zhou, Imran Memon, Md Belal Bin Heyat, Faijan Akhtar, and Xiaojuan Zhang. "View-Invariant Spatiotemporal Attentive Motion Planning and Control Network for Autonomous Vehicles." Machines 10, no. 12 (2022): 1193. http://dx.doi.org/10.3390/machines10121193.

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Autonomous driving vehicles (ADVs) are sleeping giant intelligent machines that perceive their environment and make driving decisions. Most existing ADSs are built as hand-engineered perception-planning-control pipelines. However, designing generalized handcrafted rules for autonomous driving in an urban environment is complex. An alternative approach is imitation learning (IL) from human driving demonstrations. However, most previous studies on IL for autonomous driving face several critical challenges: (1) poor generalization ability toward the unseen environment due to distribution shift pr

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