**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.

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.

Window-based Discriminator

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

DeepLab

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Gated Graph Sequence Neural Networks (GGS-NNs) is a novel graph-based neural network model. GGS-NNs modifies Graph Neural Networks (Scarselli et al., 2009) to use gated recurrent units and modern optimization techniques and then extend to output sequences.

Source: [Li et al.](https://arxiv.org/pdf/1511.05493v4.pdf)

Image source: [Li et al.](https://arxiv.org/pdf/1511.05493v4.pdf)

Source	Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com