775 research outputs found
Adversarial Variational Embedding for Robust Semi-supervised Learning
Semi-supervised learning is sought for leveraging the unlabelled data when
labelled data is difficult or expensive to acquire. Deep generative models
(e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial
Networks (GANs) have recently shown promising performance in semi-supervised
classification for the excellent discriminative representing ability. However,
the latent code learned by the traditional VAE is not exclusive (repeatable)
for a specific input sample, which prevents it from excellent classification
performance. In particular, the learned latent representation depends on a
non-exclusive component which is stochastically sampled from the prior
distribution. Moreover, the semi-supervised GAN models generate data from
pre-defined distribution (e.g., Gaussian noises) which is independent of the
input data distribution and may obstruct the convergence and is difficult to
control the distribution of the generated data. To address the aforementioned
issues, we propose a novel Adversarial Variational Embedding (AVAE) framework
for robust and effective semi-supervised learning to leverage both the
advantage of GAN as a high quality generative model and VAE as a posterior
distribution learner. The proposed approach first produces an exclusive latent
code by the model which we call VAE++, and meanwhile, provides a meaningful
prior distribution for the generator of GAN. The proposed approach is evaluated
over four different real-world applications and we show that our method
outperforms the state-of-the-art models, which confirms that the combination of
VAE++ and GAN can provide significant improvements in semisupervised
classification.Comment: 9 pages, Accepted by Research Track in KDD 201
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