Article

Winner-take-all autoencoders

Authors:

Alireza Makhzani,

Brendan FreyAuthors Info & Claims

NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2

Pages 2791 - 2799

Published: 07 December 2015 Publication History

Abstract

In this paper, we propose a winner-take-all method for learning hierarchical sparse representations in an unsupervised fashion. We first introduce fully-connected winner-take-all autoencoders which use mini-batch statistics to directly enforce a lifetime sparsity in the activations of the hidden units. We then propose the convolutional winner-take-all autoencoder which combines the benefits of convolutional architectures and autoencoders for learning shift-invariant sparse representations. We describe a way to train convolutional autoencoders layer by layer, where in addition to lifetime sparsity, a spatial sparsity within each feature map is achieved using winner-take-all activation functions. We will show that winner-take-all autoencoders can be used to to learn deep sparse representations from the MNIST, CIFAR-10, ImageNet, Street View House Numbers and Toronto Face datasets, and achieve competitive classification performance.

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https://dl.acm.org/doi/10.1007/s00138-023-01389-z
Yang QMao JWang ZHai “(2021)Dynamic Regularization on Activation Sparsity for Neural Network Efficiency ImprovementACM Journal on Emerging Technologies in Computing Systems10.1145/344777617:4(1-16)Online publication date: 30-Jun-2021
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NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2

December 2015

3626 pages

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MIT Press

Cambridge, MA, United States

Publication History

Published: 07 December 2015

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Cited By

Yeşil ÇKorkmaz E(2023)A novel cellular automata-based approach for generating convolutional filtersMachine Vision and Applications10.1007/s00138-023-01389-z34:3Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1007/s00138-023-01389-z
Yang QMao JWang ZHai “(2021)Dynamic Regularization on Activation Sparsity for Neural Network Efficiency ImprovementACM Journal on Emerging Technologies in Computing Systems10.1145/344777617:4(1-16)Online publication date: 30-Jun-2021
https://dl.acm.org/doi/10.1145/3447776
Zhu HRen YHu J(2021)Establishing A Hybrid Pieceswise Linear Model for Air Quality Prediction Based Missingness Challenges2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC52423.2021.9658931(1705-1710)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1109/SMC52423.2021.9658931
Fox IWiens J(2019)Advocacy learningProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367243.3367361(2315-2321)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367243.3367361
Majumdar ADey LChaudhury SKrishnapuram RSingla PRoy R(2019)Deeply Coupled Graph Structured Autoencoder for Domain AdaptationProceedings of the ACM India Joint International Conference on Data Science and Management of Data10.1145/3297001.3297013(94-102)Online publication date: 3-Jan-2019
https://dl.acm.org/doi/10.1145/3297001.3297013
Pang GCao LChen LLiu HGuo YFarooq F(2018)Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier DetectionProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3220042(2041-2050)Online publication date: 19-Jul-2018
https://dl.acm.org/doi/10.1145/3219819.3220042
Deng JXu XZhang ZFruhholz SSchuller BJun Deng Xinzhou Xu Zixing Zhang Fruhholz SSchuller B(2018)Semisupervised Autoencoders for Speech Emotion RecognitionIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2017.275933826:1(31-43)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1109/TASLP.2017.2759338
Kim JBukhari WLee M(2018)Feature Analysis of Unsupervised Learning for Multi-task Classification Using Convolutional Neural NetworkNeural Processing Letters10.1007/s11063-017-9724-147:3(783-797)Online publication date: 1-Jun-2018
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Chu WCai D(2017)Stacked similarity-aware autoencodersProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172104(1561-1567)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3172077.3172104
Patel ANguyen TBaraniuk R(2016)A probabilistic framework for deep learningProceedings of the 30th International Conference on Neural Information Processing Systems10.5555/3157382.3157384(2558-2566)Online publication date: 5-Dec-2016
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