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This is a set of scripts that allows for an automatic collection of 10s of thousands of images for the following (loosely defined) categories to be later used for training an image classifier
An interactive deep learning book with code, math, and discussions.
Researchers using enhanced super-resolution technology are giving classic video games of the past incredible, texture-rich visual makeovers. The team has released ‘remastered’ versions of Return to Castle Wolfenstein, Doom, The Elder Scrolls III: Morrowind, and most recently — a visually enhanced version of 2001 third-person shooter game Max Payne.
Long story short, Enhanced Super Resolution Generative Adverserial Network, or ESRGAN, is an upscaling method that is capable of generating realistic textures during single image super-resolution. Basically it's a machine learning technique that uses a generative adverserial network to upres smaller images. By doing it over several passes, it will usually produce an image with more fidelity than methods such as SRCNN and SRGAN. In fact, ESRGAN is based off SRGAN. The difference between the two is that ESRGAN improves on SRGAN's network architecture, adversarial loss and perceptual loss.
Simple artificial-intelligence problem puts researchers up against a logical paradox discovered by famed mathematician Kurt Gödel.