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Consistent Instance Classification for Unsupervised Representation Learning

In this paper, we address the problem of learning the representations from images without human annotations. We study the instance classification solution, which regards each instance as a category, and improve the optimization and feature quality. The proposed consistent instance classification (ConIC) approach simultaneously optimizes the classification loss and an additional consistency loss explicitly penalizing the feature dissimilarity between the augmented views from the same instance. The benefit of optimizing the consistency loss is that the learned features for augmented views from the same instance are more compact and accordingly the classification loss optimization becomes easier, thus boosting the quality of the learned representations. This differs from InstDisc and MoCo that use an estimated prototype as the classifier weight to ease the optimization. Different from SimCLR that directly compares different instances, our approach does not require large batch size. Experimental results demonstrate competitive performance for linear evaluation and better performance than InstDisc, MoCo and SimCLR at downstream tasks, such as detection and segmentation, as well as competitive or superior performance compared to other methods with stronger training setting.

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