A **ResNest** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks Split-Attention blocks. The cardinal group representations are then concatenated along the channel dimension: $V = \text{Concat}${$V^{1},V^{2},\cdots{V}^{K}$}. As in standard residual blocks, the final output $Y$ of otheur Split-Attention block is produced using a shortcut connection: $Y=V+X$, if the input and output feature-map share the same shape.  For blocks with a stride, an appropriate transformation $\mathcal{T}$ is applied to the shortcut connection to align the output shapes:  $Y=V+\mathcal{T}(X)$. For example, $\mathcal{T}$ can be strided [convolution](https://paperswithcode.com/method/convolution) or combined convolution-with-pooling.

The Robust Loss is a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions. By introducing robustness as a continuous parameter, the loss function allows algorithms built around robust loss minimization to be generalized, which improves performance on basic vision tasks such as registration and clustering. Interpreting the loss as the negative log of a univariate density yields a general probability distribution that includes normal and Cauchy distributions as special cases. This probabilistic interpretation enables the training of neural networks in which the robustness of the loss automatically adapts itself during training, which improves performance on learning-based tasks such as generative image synthesis and unsupervised monocular depth estimation, without requiring any manual parameter tuning.

Adaptive Loss

A General and Adaptive Robust Loss Function

ResNeSt

ResNeSt: Split-Attention Networks

**DVD-GAN DBlock** is a residual block for the discriminator used in the [DVD-GAN](https://paperswithcode.com/method/dvd-gan) architecture for video generation. Unlike regular [residual blocks](https://paperswithcode.com/method/residual-block), [3D convolutions](https://paperswithcode.com/method/3d-convolution) are employed due to the application to multiple frames in a video.

Source	ResNeSt: Split-Attention Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com