Self-Supervised Classification Network

• URL: http://arxiv.org/abs/2103.10994v1
• Date: Fri, 19 Mar 2021 19:29:42 GMT
• Title: Self-Supervised Classification Network
• Authors: Elad Amrani, Alex Bronstein
• Abstract summary: Self-supervised end-to-end classification neural network learns labels and representations simultaneously. First unsupervised end-to-end classification network to perform well on the large-scale ImageNet dataset.
• Score: 3.8073142980733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Self-Classifier -- a novel self-supervised end-to-end classification neural network. Self-Classifier learns labels and representations simultaneously in a single-stage end-to-end manner by optimizing for same-class prediction of two augmented views of the same sample. To guarantee non-degenerate solutions (i.e., solutions where all labels are assigned to the same class), a uniform prior is asserted on the labels. We show mathematically that unlike the regular cross-entropy loss, our approach avoids such solutions. Self-Classifier is simple to implement and is scalable to practically unlimited amounts of data. Unlike other unsupervised classification approaches, it does not require any form of pre-training or the use of expectation maximization algorithms, pseudo-labelling or external clustering. Unlike other contrastive learning representation learning approaches, it does not require a memory bank or a second network. Despite its relative simplicity, our approach achieves comparable results to state-of-the-art performance with ImageNet, CIFAR10 and CIFAR100 for its two objectives: unsupervised classification and unsupervised representation learning. Furthermore, it is the first unsupervised end-to-end classification network to perform well on the large-scale ImageNet dataset. Code will be made available.

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