Video: Large Scale Training for Model Optimization
by Rich Brueckner from High-Performance Computing News Analysis | insideHPC on (#3W2B9)
Jakub Tomczak from the University of Amsterdam gave this talk at PASC18. "Deep generative models allow us to learn hidden representations of data and generate new examples. There are two major families of models that are exploited in current applications: Generative Adversarial Networks (GANs), and Variational Auto-Encoders (VAE). We will point out advantages and disadvantages of GANs and VAE. Some of most promising applications of deep generative models will be shown."
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