Neural Video Encoding
Deep neural networks have had unprecedented success in computer vision, natural language processing, and speech largely due to the ability to search for suitable task algorithms via differentiable programming. In this paper, we borrow ideas from Kolmogorov complexity theory and normalizing flows to explore the possibilities of finding arbitrary algorithms that represent data. In particular, algorithms which encode sequences of video image frames. Ultimately, we demonstrate neural video encoded using convolutional neural networks to transform autoregressive noise processes and show that this method has surprising cryptographic analogs for information security.
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