Deep Image Category Discovery using a Transferred Similarity Function
This addresses the challenge of unsupervised image categorization for computer vision, though it is incremental by building on prior knowledge transfer methods.
The paper tackles the problem of automatically discovering image categories in unlabeled natural images by using transferred similarity functions from other domains, achieving 99% accuracy on MNIST and state-of-the-art results on datasets like Cifar-10 and ImageNet.
Automatically discovering image categories in unlabeled natural images is one of the important goals of unsupervised learning. However, the task is challenging and even human beings define visual categories based on a large amount of prior knowledge. In this paper, we similarly utilize prior knowledge to facilitate the discovery of image categories. We present a novel end-to-end network to map unlabeled images to categories as a clustering network. We propose that this network can be learned with contrastive loss which is only based on weak binary pair-wise constraints. Such binary constraints can be learned from datasets in other domains as transferred similarity functions, which mimic a simple knowledge transfer. We first evaluate our experiments on the MNIST dataset as a proof of concept, based on predicted similarities trained on Omniglot, showing a 99\% accuracy which significantly outperforms clustering based approaches. Then we evaluate the discovery performance on Cifar-10, STL-10, and ImageNet, which achieves both state-of-the-art accuracy and shows it can be scalable to various large natural images.