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Semi Supervised Learning (GANs) 2

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.

Thus the need to design of a PixelGAN autoencoder, Using Stacked denoising sparse autoencoder (sDSAE) that can effectively eliminate the influence of noise. Development of a mathematical connection between maximum-likelihood learning and the cost function of variational inference with implicit distributions and consider clustering task in the last step of our approach (instead of supervised learning) to discover essential patterns.

スキル: 機械学習, Matlab and Mathematica, Neural Networks, パイソン, Rプログラミング言語

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( 2件のレビュー ) Montreal, Canada

プロジェクトID: #17561119



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