DBGAN is a method for graph representation learning. Instead of the widely used normal distribution assumption, the prior distribution of latent representation in DBGAN is estimated in a structure-aware way, which implicitly bridges the graph and feature spaces by prototype learning.

Source: [Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation Learning](https://arxiv.org/abs/1912.01899)

A self-supervised learning method to disentangle irregular (anomalous) motion from regular motion in unlabeled videos.

Motion Disentanglement

Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment

DBGAN

**Weight Standardization** is a normalization technique that smooths the loss landscape by standardizing the weights in convolutional layers. Different from the previous normalization methods that focus on *activations*, WS considers the smoothing effects of *weights* more than just length-direction decoupling. Theoretically, WS reduces the Lipschitz constants of the loss and the gradients.
Hence, WS smooths the loss landscape and improves training.

In Weight Standardization, instead of directly optimizing the loss $\mathcal{L}$ on the original weights $\hat{W}$, we reparameterize the weights $\hat{W}$ as a function of $W$, i.e. $\hat{W}=\text{WS}(W)$, and optimize the loss $\mathcal{L}$ on $W$ by [SGD](https://paperswithcode.com/method/sgd):

$$
    \hat{W} = \Big[ \hat{W}\_{i,j}~\big|~ \hat{W}\_{i,j} = \dfrac{W\_{i,j} - \mu\_{W\_{i,\cdot}}}{\sigma\_{W\_{i,\cdot}+\epsilon}}\Big]
$$

$$
    y = \hat{W}*x
$$

where

$$
    \mu_{W\_{i,\cdot}} = \dfrac{1}{I}\sum\_{j=1}^{I}W\_{i, j},~~\sigma\_{W\_{i,\cdot}}=\sqrt{\dfrac{1}{I}\sum\_{i=1}^I(W\_{i,j} - \mu\_{W\_{i,\cdot}})^2}
$$

Similar to [Batch Normalization](https://paperswithcode.com/method/batch-normalization), WS controls the first and second moments of the weights of each output channel individually in convolutional layers. Note that many initialization methods also initialize the weights in some similar ways. Different from those methods, WS standardizes the weights in a differentiable way which aims to normalize gradients during back-propagation. Note that we do not have any affine transformation on $\hat{W}$. This is because we assume that normalization layers such as BN or [GN](https://paperswithcode.com/method/group-normalization) will normalize this convolutional layer again.

Year	2000
Data Source	Papers with Code archive (CC BY-SA)