Maximum Margin Linear Classifiers in Unions of Subspaces

In this work, we propose a framework, dubbed Union-of-Subspaces SVM (US-SVM), to learn linear classifiers as sparse codes over a learned dictionary. In contrast to discriminative sparse coding with a learned dictionary, it is not the data but the classifiers that are sparsely encoded. Experiments in visual categorization demonstrate that, at training time, the joint learning of the classifiers and of the over-complete dictionary allows the discovery and sharing of mid-level attributes. The resulting classifiers further have a very compact representation in the learned dictionaries, offering substantial performance advantages over standard SVM classifiers for a fixed representation sparsity. This high degree of sparsity of our classifier also provides computational gains, especially in the presence of numerous classes. In addition, the learned atoms can help identify several intra-class modalities.

Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a foundation of many multimedia retrieval systems. A common approach is to rely on Product Quantization, which allows the storage of large vector databases in memory and efficient distance computations. Yet, implementations of nearest neighbor search with Product Quantization have their...

"A recently celebrated kind of deep neural networks is Generative Adversarial Networks. GANs are generators of samples from a distribution that has been learned; they are up to now centrally trained from local data on a single location. We question the performance of training GANs using a spread dataset over...

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