Deep-Bayesian-Quadrature-Policy-Optimization

Official implementation of the AAAI 2021 paper Deep Bayesian Quadrature Policy Optimization.

EnhanceNet

Achieves realistic textures by using automated texture synthesis in combination with a perceptual loss rather than focusing on optimizing for a pixel accurate reproduction of ground truth images during training. By using feed-forward fully convolutional neural networks in an adversarial training setting, this approach achieves a significant boost in image quality at high magnification ratios.

Beta-VAE

To learn and reason like humans, AI must first learn to factorise interpretable representations of independent data generative factors (preferably in an unsupervised manner!!). What does all this mean? Go through this tutorial to get an overview of disentanglement in the context of unsupervised visual disentangled representation learning.

Voice_Style_Transfer

Attempts to perform voice transfer, inspired by Gatys et al.'s work in image domain. Uses two pre-trained networks (Wavenet and Speaker Recognition) for perceptual style and context losses.