Relevant bibliographies by topics / Fashion-MNIST dataset

Contents

  1. Journal articles
  2. Book chapters
  3. Conference papers
  4. Reports

Academic literature on the topic 'Fashion-MNIST dataset'

Author: Grafiati
Published: 7 June 2025
Last updated: 26 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Fashion-MNIST dataset.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Fashion-MNIST dataset"

1

ГНАТУШЕНКО, В. В., Т. М. ФЕНЕНКО та О. Л. ДОРОШ. "РЕЗУЛЬТАТИ НАЛАШТУВАННЯ ПАРАМЕТРІВ НЕЙРОННИХ ГЛИБОКИХ МЕРЕЖ ЩОДО РОЗПІЗНАВАННЯ FASHION MNIST DATASET". Applied Questions of Mathematical Modeling 5, № 2 (2023): 19–26. http://dx.doi.org/10.32782/mathematical-modelling/2022-5-2-2.

Full text
Abstract:
Проведено дослідження моделей згорткової нейронної мережі (Convolutional neural network – CNN) з метою підвищення точності розпізнавання Fashion MNIST DATASET. З огляду відомо, що розпізнавання елементів одягу набору Fashion MNIST є більш складним ніж розпізнавання набору рукопису цифр MNIST. Набор одягу Fashion-MNIST рекомендовано для досліджень різних архітектур нейронних мереж. Найкращі результати якості розпізнавання Fashion MNIST DATASET отримано за згортковою нейронною мережею. В даній роботі було метою покращити точність розпізнавання Fashion MNIST DATASET за рахунок дослідження різних архітектур CNN та їх параметрів. Обрано дві архітектури послідовної згорткової нейронної мережі з тих, у яких точність розпізнавання Fashion MNIST DATASET більше ніж 93%. Проведено дослідження їх архітектур та параметрів. Моделі відповідають визначенню нейронних глибоких мереж та мають різну кількість шарів. В дослідженнях моделей показано вплив параметрів batch_size, validation_split, validation_data на точність розпізнавання, а також варіанти розташування шару BatchNormalization та шару активації; вплив параметра “filters” для згорткового шару. Крім того, було використано два варіанти вибору валідаційної вибірки: перший – з набору даних для навчання (20%), а другий – набор даних тестування. При розрахунках число епох навчання дорівнювало 20. В процесі навчання вирішувалось питання не допустити перенавчання за допомогою аналізу функції втрат. Використано бібліотеки TensorFlow, Keras, мову програмування Python. Розроблено програмні модулі, які було реалізовано у хмарному сервісі Google Colab. В результаті досліджень підтверджено заявлену у роботах інших авторів точність розпізнавання >93% Fashion MNIST DATASET та отримано покращену точність розпізнавання в 94,16% для однієї з обраних моделей. Обґрунтовано вплив параметру batch_size на точність розпізнавання, обрано значення batch_size відповідно найкращому результату розпізнавання Fashion MNIST DATASET. Продемонстровано, що збільшення кількості даних для навчання покращує точність розпізнавання при використанні параметра valid_data==(X_test, X_test_ labels) замість valid_split для даних навчання. Наведені результати чисельного експеримента, які підтверджують важливість та корисність застосування методів регуляризації для вирішення проблеми перенавчання: налаштування шарів Dropout дозволило покращити точність розпізнавання.
APA, Harvard, Vancouver, ISO, and other styles
2

Kadam, Shivam S., Amol C. Adamuthe, and Ashwini B. Patil. "CNN Model for Image Classification on MNIST and Fashion-MNIST Dataset." Journal of scientific research 64, no. 02 (2020): 374–84. http://dx.doi.org/10.37398/jsr.2020.640251.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Vattikonda, Navya, Anuj Kumar Gupta, Achuthananda Reddy Polu, Bhumeka Narra, and Dheeraj Varun Kumar Reddy Buddula. "Leveraging Deep Learning for Personalized Fashion Recommendations Using Fashion MNIST." International Journal of Multidisciplinary Research in Science and Business 1, no. 07 (2024): 54–77. https://doi.org/10.63665/ijmrsb.v1i07.08.

Full text
Abstract:
People are particularly consciousof their clothing choices since fashion has a big influence on daily life. Large populations are usually recommended fashion goods and trends by specialists via a manual, curated process. On the other hand, e-commerce websites greatly benefit from automatic, personalized recommendation systems, which are becoming more popular. This study introduces a deep learning-based framework for personalized fashion recommendation, utilizing the Fashion-MNIST dataset as the primary data source. The dataset was dividedinto training and testing sets in a 70:30 ratio to ensure robust evaluation. CNN, Feedforward Neural Networks (FNN), and LSTM models were employed for fashion item classification. Evaluation metrics such as F1-score, recall, accuracy, precision, and loss, along with confusion matrix analysis, were utilized to assess model performance. Among the tested models, the CNN demonstrated superior performance, achieving 93.99% accuracy, with F1-score, recall, and precision all at 94% and a loss value of 0.2037. Comparative analysis further highlighted the CNN's effectiveness over FNN and LSTM models. These findings demonstrate the promise of CNN architectures for improving the precision and consistency of individualized clothing recommendation systems.
APA, Harvard, Vancouver, ISO, and other styles
4

Vattikonda, Navya, Anuj Kumar Gupta, Achuthananda Reddy Polu, Bhumeka Narra, and Dheeraj Varun Kumar Reddy Buddula. "Leveraging Deep Learning for Personalized Fashion Recommendations Using Fashion MNIST." International Journal of Multidisciplinary Research in Science and Business 1, no. 07 (2024): 54–77. https://doi.org/10.63665/ijmrsb.v1i01.08.

Full text
Abstract:
People are particularly consciousof their clothing choices since fashion has a big influence on daily life. Large populations are usually recommended fashion goods and trends by specialists via a manual, curated process. On the other hand, e-commerce websites greatly benefit from automatic, personalized recommendation systems, which are becoming more popular. This study introduces a deep learning-based framework for personalized fashion recommendation, utilizing the Fashion-MNIST dataset as the primary data source. The dataset was dividedinto training and testing sets in a 70:30 ratio to ensure robust evaluation. CNN, Feedforward Neural Networks (FNN), and LSTM models were employed for fashion item classification. Evaluation metrics such as F1-score, recall, accuracy, precision, and loss, along with confusion matrix analysis, were utilized to assess model performance. Among the tested models, the CNN demonstrated superior performance, achieving 93.99% accuracy, with F1-score, recall, and precision all at 94% and a loss value of 0.2037. Comparative analysis further highlighted the CNN's effectiveness over FNN and LSTM models. These findings demonstrate the promise of CNN architectures for improving the precision and consistency of individualized clothing recommendation systems.
APA, Harvard, Vancouver, ISO, and other styles
5

Mukhamediev, Ravil I. "State-of-the-Art Results with the Fashion-MNIST Dataset." Mathematics 12, no. 20 (2024): 3174. http://dx.doi.org/10.3390/math12203174.

Full text
Abstract:
In September 2024, the Fashion-MNIST dataset will be 7 years old. Proposed as a replacement for the well-known MNIST dataset, it continues to be used to evaluate machine learning model architectures. This paper describes new results achieved with the Fashion-MNIST dataset using classical machine learning models and a relatively simple convolutional network. We present the state-of-the-art results obtained using the CNN-3-128 convolutional network and data augmentation. The developed CNN-3-128 model containing three convolutional layers achieved an accuracy of 99.65% in the Fashion-MNIST test image set. In addition, this paper presents the results of computational experiments demonstrating the dependence between the number of adjustable parameters of the convolutional network and the maximum acceptable classification quality, which allows us to optimise the computational cost of model training.
APA, Harvard, Vancouver, ISO, and other styles
6

Henrique, Alisson Steffens, Anita Maria da Rocha Fernandes, Rodrigo Lyra, et al. "Classifying Garments from Fashion-MNIST Dataset Through CNNs." Advances in Science, Technology and Engineering Systems Journal 6, no. 1 (2021): 989–94. http://dx.doi.org/10.25046/aj0601109.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Navya, Vattikonda Anuj Kumar Gupta Achuthananda Reddy Polu Bhumeka Narra and Dheeraj Varun Kumar Reddy Buddula. "Leveraging Deep Learning for Personalized Fashion Recommendations Using Fashion MNIST." International Journal of Multidisciplinary Research in Science and Business 1, no. 7 (2025): 54–77. https://doi.org/10.5281/zenodo.15349661.

Full text
Abstract:
People are particularly conscious of their clothing choices since fashion has a biginfluence on daily life. Large populations are usually recommended fashion goodsand trends by specialists via a manual, curated process. On the other hand, ecommerce websites greatly benefit from automatic, personalized recommendationsystems, which are becoming more popular. This study introduces a deep learningbased framework for personalized fashion recommendation, utilizing the FashionMNIST dataset as the primary data source. The dataset was divided into trainingand testing sets in a 70:30 ratio to ensure robust evaluation. CNN, Feed forwardNeural Networks (FNN), and LSTM models were employed for fashion itemclassification. Evaluation metrics such as F1-score, recall, accuracy, precision, andloss, along with confusion matrix analysis, were utilized to assess model performance. Among the tested models, the CNN demonstrated superiorperformance, achieving 93.99% accuracy, with F1-score, recall, and precision all at94% and a loss value of 0.2037. Comparative analysis further highlighted theCNN's effectiveness over FNN and LSTM models. These findings demonstrate thepromise of CNN architectures for improving the precision and consistency ofindividualized clothing recommendation systems. 
APA, Harvard, Vancouver, ISO, and other styles
8

Navya, Vattikonda. "Leveraging Deep Learning for Personalized Fashion Recommendations Using Fashion MNIST." International Journal of Multidisciplinary Research in Science and Business 01, no. 07 (2025): 1–8. https://doi.org/10.5281/zenodo.15130896.

Full text
Abstract:
—People are particularly conscious of their clothingchoices since fashion has a big influence on daily life. Largepopulations are usually recommended fashion goods and trendsby specialists via a manual, curated process. On the other hand,e-commerce websites greatly benefit from automatic,personalized recommendation systems, which are becomingmore popular. This study introduces a deep learning-basedframework for personalized fashion recommendation, utilizingthe Fashion-MNIST dataset as the primary data source. Thedataset was divided into training and testing sets in a 70:30 ratioto ensure robust evaluation. CNN, Feedforward NeuralNetworks (FNN), and LSTM models were employed for fashionitem classification. Evaluation metrics such as F1-score, recall,accuracy, precision, and loss, along with confusion matrixanalysis, were utilized to assess model performance. Among thetested models, the CNN demonstrated superior performance,achieving 93.99% accuracy, with F1-score, recall, and precisionall at 94% and a loss value of 0.2037. Comparative analysisfurther highlighted the CNN's effectiveness over FNN andLSTM models. These findings demonstrate the promise of CNNarchitectures for improving the precision and consistency ofindividualized clothing recommendation systems
APA, Harvard, Vancouver, ISO, and other styles
9

Sumera, Sumera, R. Sirisha, Nadia Anjum, and K. Vaidehi. "Implementation of CNN and ANN for Fashion-MNIST-Dataset using Different Optimizers." Indian Journal Of Science And Technology 15, no. 47 (2022): 2639–45. http://dx.doi.org/10.17485/ijst/v15i47.1821.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Nocentini, Olivia, Jaeseok Kim, Muhammad Zain Bashir, and Filippo Cavallo. "Image Classification Using Multiple Convolutional Neural Networks on the Fashion-MNIST Dataset." Sensors 22, no. 23 (2022): 9544. http://dx.doi.org/10.3390/s22239544.

Full text
Abstract:
As the elderly population grows, there is a need for caregivers, which may become unsustainable for society. In this situation, the demand for automated help increases. One of the solutions is service robotics, in which robots have automation and show significant promise in working with people. In particular, household settings and aged people’s homes will need these robots to perform daily activities. Clothing manipulation is a daily activity and represents a challenging area for a robot. The detection and classification are key points for the manipulation of clothes. For this reason, in this paper, we proposed to study fashion image classification with four different neural network models to improve apparel image classification accuracy on the Fashion-MNIST dataset. The network models are tested with the highest accuracy with a Fashion-Product dataset and a customized dataset. The results show that one of our models, the Multiple Convolutional Neural Network including 15 convolutional layers (MCNN15), boosted the state of art accuracy, and it obtained a classification accuracy of 94.04% on the Fashion-MNIST dataset with respect to the literature. Moreover, MCNN15, with the Fashion-Product dataset and the household dataset, obtained 60% and 40% accuracy, respectively.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Fashion-MNIST dataset"

1

Kumar, Ashish, Sonia, and Digvijay Pandey. "Image classification and prediction using convolutional neural network for fashion MNIST dataset." In Artificial Intelligence and Information Technologies. CRC Press, 2024. http://dx.doi.org/10.1201/9781003510833-68.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Pundlik, Sumitra Purushottam, and Priyadarshan Dhabe. "Convolutional Neural Network-Based Garment Classification Using Fashion MNIST Dataset—A Comparative Analysis." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-6684-0_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Hayashi, Toshitaka, Dalibor Cimr, and Richard Cimler. "One-Class Classification Approach Using One-Class Classification Subtask." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240396.

Full text
Abstract:
One-class classification (OCC) is a supervised classification problem where training data includes only one class. The goal is to classify data into one class or other classes. One of the solutions is a subtask-based approach; its idea is to use the error of an arbitrary subtask. This paper conceptualizes a novel subtask-based OCC approach using an OCC subtask (OCOCC), considered with a self-labeled dataset. The core hypothesis is that OCC subtask results for one class are better than those for other classes. The proposed framework allows a recursive process to provide endless OCC subtasks. Moreover, the OCOCC could create subtasks from all OCC algorithms, including state-of-the-art (SOTA). The OCOCC is experimented with image benchmark datasets, such as MNIST, Fashion MNIST, CIFAR10, and X-ray pneumonia. The OCC subtask is executed by applying existing OCC algorithms to a self-labeled dataset created by image rotations. The positive result is that OCOCC using OCAE subtask (OCOCAE) outperformed OCAE (OCC using autoencoder) in most datasets. Exploring the OCOCC framework is a promising direction for the future of OCC.
APA, Harvard, Vancouver, ISO, and other styles
4

Hayashi, Toshitaka, Dalibor Cimr, and Richard Cimler. "Image Entropy Equalization for Autoencoder-Based One-Class Classification." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220261.

Full text
Abstract:
Autoencoder (AE) is a common technique for one-class classification (OCC). Reconstruction error (RE) is used to classify one seen class or other unseen classes. However, AE-based OCC (OCAE) does not provide a high AUC score. This study considers the hypothesis that RE is related to image entropy, and the OCAE is biased due to the image entropy differences. Based on such a hypothesis, this paper proposes image entropy equalization as the preprocessing technique. In which, image pixels are replaced by a defined set of pixels. Entropy equalized images are experimented with OCAE using MNIST, Fashion MNIST, and CIFAR10 datasets. Image Entropy Equalization improves AUC scores with several seen classes, where the improved classes have relatively high entropy on original images.
APA, Harvard, Vancouver, ISO, and other styles
5

Zhang, Jiaru, Yang Hua, Tao Song, et al. "Information Bound and Its Applications in Bayesian Neural Networks." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230617.

Full text
Abstract:
Bayesian neural networks have drawn extensive interest because of their distinctive probabilistic representation framework. However, despite its recent success, little work focuses on the information-theoretic understanding of Bayesian neural networks. In this paper, we propose Information Bound as a metric of the amount of information in Bayesian neural networks. Different from mutual information on deterministic neural networks where modification of network structure or specific input data is usually necessary, Information Bound can be easily estimated on current Bayesian neural networks without any modification of network structures or training processes. By observing the trend of Information Bound during training, we demonstrate the existence of the “critical period” in Bayesian neural networks. Besides, we show that the Information Bound can be used to judge the confidence of the model prediction and to detect out-of-distribution datasets. Based on these observations of model interpretation, we propose Information Bound regularization and Information Bound variance regularization methods. The Information Bound regularization encourages models to learn the minimum necessary information and improves the model generality and robustness. The Information Bound variance regularization encourages models to learn more about complex samples with low Information Bound. Extensive experiments on KMNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 verify the effectiveness of the proposed regularization methods.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Fashion-MNIST dataset"

1

Kumar, Mukul, Arpit Kumar Sharma, and Pramod Singh Rathore. "Enhancing Fashion Design with Conditional Generative Adversarial Networks: A CGAN Approach Using the Fashion-MNIST Dataset." In 2024 12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks (IEMECON). IEEE, 2024. https://doi.org/10.1109/iemecon62401.2024.10846744.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Snehith, Namgiri, Medavarapu T. N. D. Sri Harsha, Swarna Bodempudi, Luís Rablay Lopes Bailundo, Syed Shameem, and Jaya Vinay Namgiri. "Reconstructing Noised Images of Fashion-MNIST Dataset Using Autoencoders." In 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE). IEEE, 2023. http://dx.doi.org/10.1109/aikiie60097.2023.10390180.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Mei, Jiaming. "A comparison between CNN and leNet in fashion Mnist dataset." In 2024 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND MECHATRONICS (ICCSM 2024). AIP Publishing, 2024. https://doi.org/10.1063/5.0223181.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Kayed, Mohammed, Ahmed Anter, and Hadeer Mohamed. "Classification of Garments from Fashion MNIST Dataset Using CNN LeNet-5 Architecture." In 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE). IEEE, 2020. http://dx.doi.org/10.1109/itce48509.2020.9047776.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Xhaferra, Edmira, Elda Cina, and Luciana Toti. "Classification of Standard FASHION MNIST Dataset Using Deep Learning Based CNN Algorithms." In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, 2022. http://dx.doi.org/10.1109/ismsit56059.2022.9932737.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Li, Gordon H. Y., Christian R. Leefmans, James Williams, Robert M. Gray, Midya Parto, and Alireza Marandi. "Photonic Neural Cellular Automata for Self-Organized Image Classification." In CLEO: Science and Innovations. Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_si.2023.sth3f.1.

Full text
Abstract:
Neural networks based on Cellular Automata (CA) have recently yielded more robust, reliable, and parameter-efficient machine learning models. We experimentally demonstrate the first photonic implementation of CA which successfully performs image classification on the Fashion-MNIST dataset.
APA, Harvard, Vancouver, ISO, and other styles
7

Kumar, Teerath, Rob Brennan, and Malika Bendechache. "Stride Random Erasing Augmentation." In 6th International Conference on Artificial Intelligence, Soft Computing and Applications (AISCA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120201.

Full text
Abstract:
This paper presents a new method for data augmentation called Stride Random Erasing Augmentation (SREA) to improve classification performance. In SREA, probability based strides of one image are pasted onto another image and also labels of both images are mixed with the same probability as the image mixing, to generate a new augmented image and augmented label. Stride augmentation overcomes limitations of the popular random erasing data augmentation method, where a random portion of an image is erased with 0 or 255 or the mean of a dataset without considering the location of the important feature(s) within the image. A variety of experiments have been performed using different network flavours and the popular datasets including fashion-MNIST, CIFAR10, CIFAR100 and STL10. The experiments showed that SREA is more generalized than both the baseline and random erasing method. Furthermore, the effect of stride size in SREA was investigated by performing experiments with different stride sizes. Random stride size showed better performance. SREA outperforms the baseline and random erasing especially on the fashion-MNIST dataset. To enable the reuse, reproduction and extension of SREA, the source code is provided in a public git repository https://github.com/kmr2017/stride-aug.
APA, Harvard, Vancouver, ISO, and other styles
8

Cheng, Xiang, Yunzhe Hao, Jiaming Xu, and Bo Xu. "LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition." 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.2020/211.

Full text
Abstract:
Spiking Neural Network (SNN) is considered more biologically plausible and energy-efficient on emerging neuromorphic hardware. Recently backpropagation algorithm has been utilized for training SNN, which allows SNN to go deeper and achieve higher performance. However, most existing SNN models for object recognition are mainly convolutional structures or fully-connected structures, which only have inter-layer connections, but no intra-layer connections. Inspired by Lateral Interactions in neuroscience, we propose a high-performance and noise-robust Spiking Neural Network (dubbed LISNN). Based on the convolutional SNN, we model the lateral interactions between spatially adjacent neurons and integrate it into the spiking neuron membrane potential formula, then build a multi-layer SNN on a popular deep learning framework, i.\,e., PyTorch. We utilize the pseudo-derivative method to solve the non-differentiable problem when applying backpropagation to train LISNN and test LISNN on multiple standard datasets. Experimental results demonstrate that the proposed model can achieve competitive or better performance compared to current state-of-the-art spiking neural networks on MNIST, Fashion-MNIST, and N-MNIST datasets. Besides, thanks to lateral interactions, our model processes stronger noise-robustness than other SNN. Our work brings a biologically plausible mechanism into SNN, hoping that it can help us understand the visual information processing in the brain.
APA, Harvard, Vancouver, ISO, and other styles
9

Martínez Beltrán, Enrique Tomás, Pedro Miguel Sánchez Sánchez, Sergio López Bernal, et al. "Fedstellar: A Platform for Training Models in a Privacy-preserving and Decentralized Fashion." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/838.

Full text
Abstract:
This paper presents Fedstellar, a platform for training decentralized Federated Learning (FL) models in heterogeneous topologies in terms of the number of federation participants and their connections. Fedstellar allows users to build custom topologies, enabling them to control the aggregation of model parameters in a decentralized manner. The platform offers a Web application for creating, managing, and connecting nodes to ensure data privacy and provides tools to measure, monitor, and analyze the performance of the nodes. The paper describes the functionalities of Fedstellar and its potential applications. To demonstrate the applicability of the platform, different use cases are presented in which decentralized, semi-decentralized, and centralized architectures are compared in terms of model performance, convergence time, and network overhead when collaboratively classifying hand-written digits using the MNIST dataset.
APA, Harvard, Vancouver, ISO, and other styles
10

Sun, Lichao, Jianwei Qian, and Xun Chen. "LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/217.

Full text
Abstract:
Training deep learning models on sensitive user data has raised increasing privacy concerns in many areas. Federated learning is a popular approach for privacy protection that collects the local gradient information instead of raw data. One way to achieve a strict privacy guarantee is to apply local differential privacy into federated learning. However, previous works do not give a practical solution due to two issues. First, the range difference of weights in different deep learning model layers has not been explicitly considered when applying local differential privacy mechanism. Second, the privacy budget explodes due to the high dimensionality of weights in deep learning models and many query iterations of federated learning. In this paper, we proposed a novel design of local differential privacy mechanism for federated learning to address the abovementioned issues. It makes the local weights update differentially private by adapting to the varying ranges at different layers of a deep neural network, which introduces a smaller variance of the estimated model weights, especially for deeper models. Moreover, the proposed mechanism bypasses the curse of dimensionality by parameter shuffling aggregation. A series of empirical evaluations on three commonly used datasets in prior differential privacy works, MNIST, Fashion-MNIST and CIFAR-10, demonstrate that our solution can not only achieve superior deep learning performance but also provide a strong privacy guarantee at the same time.
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Fashion-MNIST dataset"

1

Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

Full text
Abstract:
We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, comparing it against standard CNNs, CNN-KAN hybrids, and ICB variants. KANICE consistently outperformed baseline models, achieving 99.35% accuracy on MNIST and 90.05% on the SVHN dataset. Furthermore, we introduce KANICE-mini, a compact variant designed for efficiency. A comprehensive ablation study demonstrates that KANICE-mini achieves comparable performance to KANICE with significantly fewer parameters. KANICE-mini reached 90.00% accuracy on SVHN with 2,337,828 parameters, compared to KAN-ICE’s 25,432,000. This study highlights the potential of KAN-based architectures in balancing performance and computational efficiency in image classification tasks. Our work contributes to research in adaptive neural networks, integrates mathematical theorems into deep learning architectures, and explores the trade-offs between model complexity and performance, advancing computer vision and pattern recognition. The source code for this paper is publicly accessible through our GitHub repository (https://github.com/mferdaus/kanice).
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Fashion-MNIST dataset' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography