Disentangled Variational Auto-Encoder for Semi-supervised Learning
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In this paper, we develop a novel approach for semi-supervised VAE without classifier. Specifically, we propose a new model called SDVAE, which encodes the input data into disentangled representation and non-interpretable representation, then the category information is directly utilized to regularize the disentangled representation via equation constraint. To further enhance the feature learning ability of the proposed VAE, we incorporate reinforcement learning to relieve the lack of data. The dynamic framework is capable of dealing with both image and text data with its corresponding encoder and decoder networks. Extensive experiments on image and text datasets demonstrate the effectiveness of the proposed framework.
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