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Pixel Generative Adversarial Network

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.

Skills: Image Processing, Keras, Python, Pytorch, Tensorflow

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About the Employer:
( 0 reviews ) Montreal, Canada

Project ID: #18023449

10 freelancers are bidding on average $518 for this job

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