A **Window-based Discriminator** is a type of discriminator for generative adversarial networks. It is analogous to a [PatchGAN](https://paperswithcode.com/method/patchgan) but designed for audio. While a standard [GAN](https://paperswithcode.com/method/gan) discriminator learns to classify between distributions of entire audio sequences, window-based discriminator learns to classify between distribution of small audio chunks. Since the discriminator loss is computed over the overlapping windows where each window is very large (equal to the receptive field of the discriminator), the model learns to maintain coherence across patches.

**Protagonist Antagonist Induced Regret Environment Design**, or **PAIRED**, is an adversarial method for approximate minimax regret to generate environments for reinforcement learning. It introduces an antagonist which is allied with the environment generating adversary. The primary agent we are trying to train is the protagonist. The environment adversary’s goal is to design environments in which the antagonist achieves high reward and the protagonist receives low reward. If the adversary generates unsolvable environments, the antagonist and protagonist would perform the same and the adversary would get a score of zero, but if the adversary finds environments the antagonist solves and the protagonist does not solve, the adversary achieves a positive score. Thus, the environment adversary is incentivized to create challenging but feasible environments, in which the antagonist can outperform the protagonist. Moreover, as the protagonist learns to solves the simple environments, the antagonist must generate more complex environments to make the protagonist fail, increasing the complexity of the generated tasks and leading to automatic curriculum generation.

Protagonist Antagonist Induced Regret Environment Design

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

Window-based Discriminator

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

**DeepLab** is a semantic segmentation architecture. First, the input image goes through the network with the use of dilated convolutions. Then the output from the network is bilinearly interpolated and goes through the fully connected [CRF](https://paperswithcode.com/method/crf) to fine tune the result we obtain the final predictions.

Source	MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com