Relevant bibliographies by topics / Encoder and decoder feature

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Encoder and decoder feature'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Encoder and decoder feature"

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Shim, Jae-hun, Hyunwoo Yu, Kyeongbo Kong, and Suk-Ju Kang. "FeedFormer: Revisiting Transformer Decoder for Efficient Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 2263–71. http://dx.doi.org/10.1609/aaai.v37i2.25321.

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With the success of Vision Transformer (ViT) in image classification, its variants have yielded great success in many downstream vision tasks. Among those, the semantic segmentation task has also benefited greatly from the advance of ViT variants. However, most studies of the transformer for semantic segmentation only focus on designing efficient transformer encoders, rarely giving attention to designing the decoder. Several studies make attempts in using the transformer decoder as the segmentation decoder with class-wise learnable query. Instead, we aim to directly use the encoder features as
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Wen, Ying, Kai Xie, and Lianghua He. "Segmenting Medical MRI via Recurrent Decoding Cell." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12452–59. http://dx.doi.org/10.1609/aaai.v34i07.6932.

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The encoder-decoder networks are commonly used in medical image segmentation due to their remarkable performance in hierarchical feature fusion. However, the expanding path for feature decoding and spatial recovery does not consider the long-term dependency when fusing feature maps from different layers, and the universal encoder-decoder network does not make full use of the multi-modality information to improve the network robustness especially for segmenting medical MRI. In this paper, we propose a novel feature fusion unit called Recurrent Decoding Cell (RDC) which leverages convolutional R
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Sun, Jun, Junbo Zhang, Xuesong Gao, et al. "Fusing Spatial Attention with Spectral-Channel Attention Mechanism for Hyperspectral Image Classification via Encoder–Decoder Networks." Remote Sensing 14, no. 9 (2022): 1968. http://dx.doi.org/10.3390/rs14091968.

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In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. However, feature extraction on hyperspectral data still faces numerous challenges. Existing methods cannot extract spatial and spectral-channel contextual information in a targeted manner. In this paper, we propose an encoder–decoder network that fuses spatial attention and spectral-channel attention for HSI classification from three public HSI datasets to tackle these issues. In terms of feature information fusion, a multi-source attention mechanism including spatial and sp
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Alharbi, Majed, Ahmed Stohy, Mohammed Elhenawy, Mahmoud Masoud, and Hamiden El-Wahed Khalifa. "Solving Traveling Salesman Problem with Time Windows Using Hybrid Pointer Networks with Time Features." Sustainability 13, no. 22 (2021): 12906. http://dx.doi.org/10.3390/su132212906.

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This paper introduces a time efficient deep learning-based solution to the traveling salesman problem with time window (TSPTW). Our goal is to reduce the total tour length traveled by -*the agent without violating any time limitations. This will aid in decreasing the time required to supply any type of service, as well as lowering the emissions produced by automobiles, allowing our planet to recover from air pollution emissions. The proposed model is a variation of the pointer networks that has a better ability to encode the TSPTW problems. The model proposed in this paper is inspired from our
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Ai, Xinbo, Yunhao Xie, Yinan He, and Yi Zhou. "Improve SegNet with feature pyramid for road scene parsing." E3S Web of Conferences 260 (2021): 03012. http://dx.doi.org/10.1051/e3sconf/202126003012.

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Road scene parsing is a common task in semantic segmentation. Its images have characteristics of containing complex scene context and differing greatly among targets of the same category from different scales. To address these problems, we propose a semantic segmentation model combined with edge detection. We extend the segmentation network with an encoder-decoder structure by adding an edge feature pyramid module, namely Edge Feature Pyramid Network (EFPNet, for short). This module uses edge detection operators to get boundary information and then combines the multiscale features to improve t
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Jiang, S. L., G. Li, W. Yao, Z. H. Hong, and T. Y. Kuc. "DUAL PYRAMIDS ENCODER-DECODER NETWORK FOR SEMANTIC SEGMENTATION IN GROUND AND AERIAL VIEW IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 605–10. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-605-2020.

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Abstract. Semantic segmentation is a fundamental research task in computer vision, which intends to assign a certain category to every pixel. Currently, most existing methods only utilize the deepest feature map for decoding, while high-level features get inevitably lost during the procedure of down-sampling. In the decoder section, transposed convolution or bilinear interpolation was widely used to restore the size of the encoded feature map; however, few optimizations are applied during up-sampling process which is detrimental to the performance for grouping and classification. In this work,
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Abdulaziz AlArfaj, Abeer, and Hanan Ahmed Hosni Mahmoud. "A Moving Object Tracking Technique Using Few Frames with Feature Map Extraction and Feature Fusion." ISPRS International Journal of Geo-Information 11, no. 7 (2022): 379. http://dx.doi.org/10.3390/ijgi11070379.

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Moving object tracking techniques using machine and deep learning require large datasets for neural model training. New strategies need to be invented that utilize smaller data training sizes to realize the impact of large-sized datasets. However, current research does not balance the training data size and neural parameters, which creates the problem of inadequacy of the information provided by the low visual data content for parameter optimization. To enhance the performance of moving object tracking that appears in only a few frames, this research proposes a deep learning model using an abu
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Wang, Hongquan, Xinshan Zhu, Chao Ren, Lan Zhang, and Shugen Ma. "A Frequency Attention-Based Dual-Stream Network for Image Inpainting Forensics." Mathematics 11, no. 12 (2023): 2593. http://dx.doi.org/10.3390/math11122593.

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The rapid development of digital image inpainting technology is causing serious hidden danger to the security of multimedia information. In this paper, a deep network called frequency attention-based dual-stream network (FADS-Net) is proposed for locating the inpainting region. FADS-Net is established by a dual-stream encoder and an attention-based blue-associative decoder. The dual-stream encoder includes two feature extraction streams, the raw input stream (RIS) and the frequency recalibration stream (FRS). RIS directly captures feature maps from the raw input, while FRS performs feature ext
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Li, Xin, Feng Xu, Runliang Xia, et al. "Encoding Contextual Information by Interlacing Transformer and Convolution for Remote Sensing Imagery Semantic Segmentation." Remote Sensing 14, no. 16 (2022): 4065. http://dx.doi.org/10.3390/rs14164065.

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Contextual information plays a pivotal role in the semantic segmentation of remote sensing imagery (RSI) due to the imbalanced distributions and ubiquitous intra-class variants. The emergence of the transformer intrigues the revolution of vision tasks with its impressive scalability in establishing long-range dependencies. However, the local patterns, such as inherent structures and spatial details, are broken with the tokenization of the transformer. Therefore, the ICTNet is devised to confront the deficiencies mentioned above. Principally, ICTNet inherits the encoder–decoder architecture. Fi
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Geng, Yaogang, Hongyan Mei, Xiaorong Xue, and Xing Zhang. "Image-Caption Model Based on Fusion Feature." Applied Sciences 12, no. 19 (2022): 9861. http://dx.doi.org/10.3390/app12199861.

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The encoder–decoder framework is the main frame of image captioning. The convolutional neural network (CNN) is usually used to extract grid-level features of the image, and the graph convolutional neural network (GCN) is used to extract the image’s region-level features. Grid-level features are poor in semantic information, such as the relationship and location of objects, while regional features lack fine-grained information about images. To address this problem, this paper proposes a fusion-features-based image-captioning model, which includes the fusion feature encoder and LSTM decoder. The
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Dissertations / Theses on the topic "Encoder and decoder feature"

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Kalchbrenner, Nal. "Encoder-decoder neural networks." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:d56e48db-008b-4814-bd82-a5d612000de9.

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This thesis introduces the concept of an encoder-decoder neural network and develops architectures for the construction of such networks. Encoder-decoder neural networks are probabilistic conditional generative models of high-dimensional structured items such as natural language utterances and natural images. Encoder-decoder neural networks estimate a probability distribution over structured items belonging to a target set conditioned on structured items belonging to a source set. The distribution over structured items is factorized into a product of tractable conditional distributions over in
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Padinjare, Sainath. "VLSI implementation of a turbo encoder/decoder /." Internet access available to MUN users only, 2003. http://collections.mun.ca/u?/theses,162832.

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Weitzman, Jonathan M. "SELECTABLE PERMUTATION ENCODER/DECODER FOR A QPSK MODEM." International Foundation for Telemetering, 2003. http://hdl.handle.net/10150/605817.

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International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada<br>An artifact of QPSK modems is ambiguity of the recovered data. There are four variations of the output data for a given input data stream. All are equally probable. To resolve this ambiguity, the QPSK data streams can be differentially encoded before modulation and differentially decoded after demodulation. The encoder maps each input data pair to a phase angle change of the QPSK carrier. In the demodulator, the inverse is performed - each phase change of the inpu
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Mejdi, Sami. "Encoder-Decoder Networks for Cloud Resource Consumption Forecasting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291546.

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Excessive resource allocation in telecommunications networks can be prevented by forecasting the resource demand when dimensioning the networks and the allocation the necessary resources accordingly, which is an ongoing effort to achieve a more sustainable development. In this work, traffic data from cloud environments that host deployed virtualized network functions (VNFs) of an IP Multimedia Subsystem (IMS) has been collected along with the computational resource consumption of the VNFs. A supervised learning approach was adopted to address the forecasting problem by considering encoder-deco
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Mejdi, Sami. "Encoder-Decoder Networks for Cloud Resource Consumption Forecasting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294066.

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Excessive resource allocation in telecommunications networks can be prevented by forecasting the resource demand when dimensioning the networks and then allocating the necessary resources accordingly, which is an ongoing effort to achieve a more sustainable development. In this work, traffic data from cloud environments that host deployed virtualized network functions (VNFs) of an IP Multimedia Subsystem (IMS) has been collected along with the computational resource consumption of the VNFs. A supervised learning approach was adopted to address the forecasting problem by considering encoder-dec
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Correia, Tiago Miguel Pina. "FPGA implementation of Alamouti encoder/decoder for LTE." Master's thesis, Universidade de Aveiro, 2013. http://hdl.handle.net/10773/12679.

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Mestrado em Engenharia Electrónica e Telecomunicações<br>Motivados por transmissões mais rápidas e mais fiáveis num canal sem fios, os sistemas da 4G devem proporcionar processamento de dados mais rápido a baixa complexidade, elevadas taxas de dados, assim como robustez na performance reduzindo também, a latência e os custos de operação. LTE apresenta, na sua camada física, tecnologias como OFDM e MIMO que prometem alcançar elevadas taxas de dados e aumentar a eficiência espectral. Especificamente a camada física do LTE emprega OFDMA para downlink e SC-FDMA para uplink. A tecnologia MIMO per
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Leivas, Oliveira Gabriel [Verfasser], Thomas [Akademischer Betreuer] Brox, and Wolfram [Akademischer Betreuer] Burgard. "Encoder-decoder methods for semantic segmentation: efficiency and robustness aspects." Freiburg : Universität, 2019. http://d-nb.info/1191689476/34.

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Kopparthi, Sunitha. "Flexible encoder and decoder designs for low-density parity-check codes." Diss., Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/4190.

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Pisacane, Claudia. "Skopos Theory La figura del traduttore come decoder e re-encoder." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8926/.

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La Skopos Theory è una teoria introdotta nel mondo della traduzione dal linguista tedesco Hans Joseph Vermeer. Skopos è una parola di derivazione greca che significa “fine” o “scopo”. La teoria elaborata da Vermeer si basa sull’idea che ogni testo abbia uno skopos che determina i metodi e le strategie secondo le quali esso debba essere tradotto. Oltre alla Skopos Theory, che sarà la base della tesi, i testi a seguire verranno analizzati seguendo altri autori, quali Mona Baker e Laurence Venuti, che si rifanno all’idea di skopos e analizzano molto dettagliatamente la figura del traduttore come
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Nina, Oliver A. Nina. "A Multitask Learning Encoder-N-Decoder Framework for Movie and Video Description." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531996548147165.

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Books on the topic "Encoder and decoder feature"

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Jet Propulsion Laboratory (U.S.), ed. A software simulation study of a (255,223) Reed-Solomon encoder/decoder. National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1985.

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Sari, Mehmet. Designing fast Golay encoder/decoder in Xilinx XACT with Mentor Graphics CAD interface. Naval Postgraduate School, 1997.

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Madadi, H. Assessment of a wide area digital paging system (microprocessor based decoder/encoder) and VHF Channel characterization for urban and suburban areas. University of Birmingham, 1985.

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McKenzie, Stephen Scott. A systolic array implementation of a Reed-Solomon encoder and decoder. 1985.

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Bluedorn, Harvey. Handy English Encoder Decoder: All the Spelling and Phonics Rules You Could Ever Want to Know. Trivium Pursuit, 2004.

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Wireless Radio Frequency Module Using PIC Microcontroller.: The Basics of Wireless Radio Frequency Communications With Using Latest & An Advanced MCU Named PIC. Public Domain, 2012.

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Lamel, Lori, and Jean-Luc Gauvain. Speech Recognition. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0016.

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Speech recognition is concerned with converting the speech waveform, an acoustic signal, into a sequence of words. Today's approaches are based on a statistical modellization of the speech signal. This article provides an overview of the main topics addressed in speech recognition, which are, acoustic-phonetic modelling, lexical representation, language modelling, decoding, and model adaptation. Language models are used in speech recognition to estimate the probability of word sequences. The main components of a generic speech recognition system are, main knowledge sources, feature analysis, a
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Book chapters on the topic "Encoder and decoder feature"

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Gong, Yansheng, and Wenfeng Jing. "A Fully-Nested Encoder-Decoder Framework for Anomaly Detection." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_75.

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AbstractAnomaly detection is an important branch of computer vision. At present, a variety of deep learning models are applied to anomaly detection. However, the lack of abnormal samples makes supervised learning difficult to implement. In this paper, we mainly study abnormal detection tasks based on unsupervised learning and propose a Fully-Nested Encoder-decoder Framework. The main part of the proposed generating model consists of a generator and a discriminator, which are adversarially trained based on normal data samples. In order to improve the image reconstruction capability of the generator, we design a Fully-Nested Residual Encoder-decoder Network, which is used to encode and decode the images. In addition, we add residual structure into both encoder and decoder, which reduces the risk of overfitting and enhances the feature expression ability. In the test phase, a distance measurement model is used to determine whether the test sample is abnormal. The experimental results on the CIFAR-10 dataset demonstrate the excellent performance of our method. Compared with the existing models, our method achieves the state-of-the-art result.
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Liu, Hongyu, Bin Jiang, Yibing Song, Wei Huang, and Chao Yang. "Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58536-5_43.

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Li, Mingxiao. "A High-Efficiency Knowledge Distillation Image Caption Technology." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_92.

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AbstractImage caption is wildly considered in the application of machine learning. Its purpose is describing one given picture into text accurately. Currently, it uses the Encoder-Decoder architecture from deep learning. To further increase the semantic transmitted after distillation by feature representation, this paper proposes a knowledge distillation framework to increase the results of the teacher section, extracting features by different semantic levels from different fields of view, and the loss function adopts the method of label normalization. Handle unmatched image-sentence pairs. In order to achieve the purpose of a more efficient process. Experimental results prove that this knowledge distillation architecture can strengthen the semantic information transmitted after distillation in the feature representation, achieve a more efficient training model on less data, and obtain a higher accuracy rate.
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Liu, Hongyu, Bin Jiang, Yibing Song, Wei Huang, and Chao Yang. "Correction to: Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58536-5_47.

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Duan, Lijuan, Xuan Feng, Jie Chen, and Fan Xu. "An Automated Method with Feature Pyramid Encoder and Dual-Path Decoder for Nuclei Segmentation." In Pattern Recognition and Computer Vision. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60633-6_28.

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Qiu, Yi, Long Cheng, Man Xu, Jing Chen, and Hongjie Wu. "Feature Extraction Approach for Predicting Protein-DNA Binding Residues Using Transformer Encoder-Decoder Architecture." In Advanced Intelligent Computing in Bioinformatics. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5689-6_21.

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Li, Zihan, Wei Ding, Inal Mashukov, Scott Crouter, and Ping Chen. "A Multi-view Feature Construction and Multi-Encoder-Decoder Transformer Architecture for Time Series Classification." In Advances in Knowledge Discovery and Data Mining. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2266-2_19.

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Lu, Xuesong, and Yuchuan Qiao. "Multi-channel Image Registration of Cardiac MR Using Supervised Feature Learning with Convolutional Encoder-Decoder Network." In Biomedical Image Registration. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50120-4_10.

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Sellami, Akrem, and Salvatore Tabbone. "EDNets: Deep Feature Learning for Document Image Classification Based on Multi-view Encoder-Decoder Neural Networks." In Document Analysis and Recognition – ICDAR 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86337-1_22.

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Yang, Liu, Boyu Wang, Jack C. P. Cheng, Peipei Liu, and Hoon Sohn. "Real-Time Geometry Assessment Using Laser Line Scanner During Laser Powder Directed Energy Deposition Additive Manufacturing of SS316L Component with Sharp Feature." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.97.

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Directed energy deposition (DED) is a major metal additive manufacturing (AM) technology that is increasingly used in many industries due to its ability to manufacture complex components of arbitrary shapes and sizes. However, a lack of timely geometry assessment and the consequent geometry control hinders the development of DED towards zero defect manufacturing. In this study, a real-time geometry assessment methodology is developed for laser pow-der directed energy deposition (LP-DED). A geometry assessment system is developed using a laser line scanner capable of inspecting the melt pool area, the just solidified area, as well as layer-wise inspection. An image processing method with an encoder-decoder based profile completion network was developed to obtain accurate track profile in images from real-time inspection. Experiments have been conducted to validate the proposed methodology by depositing multi-layer X-shape objects
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Conference papers on the topic "Encoder and decoder feature"

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Ramon, Raz, Hadar Cohen-Duwek, and Elishai Ezra Tsur. "ED-DCFNet: an unsupervised encoder-decoder neural model for event-driven feature extraction and object tracking." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00224.

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Liu, Boyu. "Comparative Analysis of Encoder-Only, Decoder-Only, and Encoder- Decoder Language Models." In International Conference on Data Science and Engineering. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012829800004547.

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Zhang, Jinchao, Qun Liu, and Jie Zhou. "ME-MD: An Effective Framework for Neural Machine Translation with Multiple Encoders and Decoders." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/474.

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The encoder-decoder neural framework is widely employed for Neural Machine Translation (NMT) with a single encoder to represent the source sentence and a single decoder to generate target words. The translation performance heavily relies on the representation ability of the encoder and the generation ability of the decoder. To further enhance NMT, we propose to extend the original encoder-decoder framework to a novel one, which has multiple encoders and decoders (ME-MD). Through this way, multiple encoders extract more diverse features to represent the source sequence and multiple decoders cap
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SharifiPour, Sasan, Hossein Fayyazi, and Mohammad Sabokro. "Unsupervised Feature Selection using Encoder-Decoder Networks." In 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). IEEE, 2020. http://dx.doi.org/10.1109/icspis51611.2020.9349608.

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Wang, Qixiang, Shanfeng Wang, Maoguo Gong, and Yue Wu. "Feature Hashing for Network Representation Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/390.

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The goal of network representation learning is to embed nodes so as to encode the proximity structures of a graph into a continuous low-dimensional feature space. In this paper, we propose a novel algorithm called node2hash based on feature hashing for generating node embeddings. This approach follows the encoder-decoder framework. There are two main mapping functions in this framework. The first is an encoder to map each node into high-dimensional vectors. The second is a decoder to hash these vectors into a lower dimensional feature space. More specifically, we firstly derive a proximity mea
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Yu, Yunlong, Dingyi Zhang, and Zhong Ji. "Masked Feature Generation Network for Few-Shot Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/513.

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In this paper, we present a feature-augmentation approach called Masked Feature Generation Network (MFGN) for Few-Shot Learning (FSL), a challenging task that attempts to recognize the novel classes with a few visual instances for each class. Most of the feature-augmentation approaches tackle FSL tasks via modeling the intra-class distributions. We extend this idea further to explicitly capture the intra-class variations in a one-to-many manner. Specifically, MFGN consists of an encoder-decoder architecture, with an encoder that performs as a feature extractor and extracts the feature embeddin
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Chen, Xueying, Rong Zhang, and Pingkun Yan. "Feature Fusion Encoder Decoder Network for Automatic Liver Lesion Segmentation." In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI). IEEE, 2019. http://dx.doi.org/10.1109/isbi.2019.8759555.

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Tao, Zhiqiang, Hongfu Liu, Jun Li, Zhaowen Wang, and Yun Fu. "Adversarial Graph Embedding for Ensemble Clustering." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/494.

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Ensemble clustering generally integrates basic partitions into a consensus one through a graph partitioning method, which, however, has two limitations: 1) it neglects to reuse original features; 2) obtaining consensus partition with learnable graph representations is still under-explored. In this paper, we propose a novel Adversarial Graph Auto-Encoders (AGAE) model to incorporate ensemble clustering into a deep graph embedding process. Specifically, graph convolutional network is adopted as probabilistic encoder to jointly integrate the information from feature content and consensus graph, a
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Liu, Yang, Deyan Xie, Quanxue Gao, Jungong Han, Shujian Wang, and Xinbo Gao. "Graph and Autoencoder Based Feature Extraction for Zero-shot Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/421.

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Zero-shot learning (ZSL) aims to build models to recognize novel visual categories that have no associated labelled training samples. The basic framework is to transfer knowledge from seen classes to unseen classes by learning the visual-semantic embedding. However, most of approaches do not preserve the underlying sub-manifold of samples in the embedding space. In addition, whether the mapping can precisely reconstruct the original visual feature is not investigated in-depth. In order to solve these problems, we formulate a novel framework named Graph and Autoencoder Based Feature Extraction
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Lin, Zhihui, Chun Yuan, and Maomao Li. "HAF-SVG: Hierarchical Stochastic Video Generation with Aligned Features." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/138.

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Stochastic video generation methods predict diverse videos based on observed frames, where the main challenge lies in modeling the complex future uncertainty and generating realistic frames. Numerous of Recurrent-VAE-based methods have achieved state-of-the-art results. However, on the one hand, the independence assumption of the variables of approximate posterior limits the inference performance. On the other hand, although these methods adopt skip connections between encoder and decoder to utilize multi-level features, they still produce blurry generation due to the spatial misalignment betw
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Reports on the topic "Encoder and decoder feature"

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Johnson, Zachary. Heatmap Router: An encoder-decoder approach to PCB routing. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-819.

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Ramakrishnan, Aravind, Fangyu Liu, Angeli Jayme, and Imad Al-Qadi. Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Model. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-030.

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For robust pavement design, accurate damage computation is essential, especially for loading scenarios such as truck platoons. Studies have developed a framework to compute pavement distresses as function of lateral position, spacing, and market-penetration level of truck platoons. The established framework uses a robust 3D pavement model, along with the AASHTOWare Mechanistic–Empirical Pavement Design Guidelines (MEPDG) transfer functions to compute pavement distresses. However, transfer functions include high variability and lack physical significance. Therefore, as an improvement to effecti
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Patwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.

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First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose t
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