Learning To Classify Images Without Labels
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Is it possible to automatically classify images without the use of ground-truth annotations? Or when even the classes themselves, are not a priori known? These remain important, and open questions in computer vision. Several approaches have tried to tackle this problem in an end-to-end fashion. In this paper, we deviate from recent works, and advocate a two-step approach where feature learning and clustering are decoupled. First, a self-supervised task from representation learning is employed to obtain semantically meaningful features. Second, we use the obtained features as a prior in a learnable clustering approach. In doing so, we remove the ability for cluster learning to depend on low-level features, which is present in current end-to-end learning approaches. Experimental evaluation shows that we outperform state-of-the-art methods by huge margins, in particular +26.9 and +11.7 on ImageNet show that our approach is the first to scale well up to 200 randomly selected classes, obtaining 69.3 marking a difference of less than 7.5 we applied our approach to all 1000 classes on ImageNet, and found the results to be very encouraging. The code will be made publicly available.
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