Semi-supervised Learning. Semi-supervised learning is a long-standing and important
problem in Machine Learning. One of the main goals of generative modeling is to discover useful representations that can be used in the downstream tasks such as semi-supervised learning. The second problem is that these models tend to use all their capacity to capture the low-level pixel statistics, and hence the generated images often have little recognizable global structure. In order to address both these problems, we proposed the PixelGAN autoencoder, which combines the benefits of latent variable models with autoregressive architectures. We showed that the latent variable in PixelGAN autoencoders captures the global structure of the image while the autoregressive decoder captures the local statistics of the image.
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hello sir , I am a researcher at multinational companyy and i have been working in GANs recently i have been working on machine learning projects. I am looking forward to discuss this with you.