Distribution Networks for Open Set Learning
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In open set learning, a model must be able to generalize to novel classes when it encounters a sample that does not belong to any of the classes it has seen before. Open set learning poses a realistic learning scenario that is receiving growing attention. Existing studies on open set learning mainly focused on detecting novel classes, but few studies tried to model them for differentiating novel classes. We recognize that novel classes should be different from each other, and propose distribution networks for open set learning that can learn and model different novel classes. We hypothesize that, through a certain mapping, samples from different classes with the same classification criterion should follow different probability distributions from the same distribution family. We estimate the probability distribution for each known class and a novel class is detected when a sample is not likely to belong to any of the known distributions. Due to the large feature dimension in the original feature space, the probability distributions in the original feature space are difficult to estimate. Distribution networks map the samples in the original feature space to a latent space where the distributions of known classes can be jointly learned with the network. In the latent space, we also propose a distribution parameter transfer strategy for novel class detection and modeling. By novel class modeling, the detected novel classes can serve as known classes to the subsequent classification. Our experimental results on image datasets MNIST and CIFAR10 and text dataset Ohsumed show that the distribution networks can detect novel classes accurately and model them well for the subsequent classification tasks.
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