Learnable graph convolutional layer (LGCL) automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs.

Description and image from: [Large-Scale Learnable Graph Convolutional Networks](https://arxiv.org/pdf/1808.03965.pdf)

A **TResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that aim to boost accuracy while maintaining GPU training and inference efficiency.  They contain several design tricks including a SpaceToDepth stem, [Anti-Alias downsampling](https://paperswithcode.com/method/anti-alias-downsampling), In-Place Activated BatchNorm, Blocks selection and squeeze-and-excitation layers.

TResNet

TResNet: High Performance GPU-Dedicated Architecture

LGCL

Large-Scale Learnable Graph Convolutional Networks

**CAMoE** is a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (MoE) for video-text retrieval. The CAMoE employs Mixture-of-Experts (MoE) to extract multi-perspective video representations, including action, entity, scene, etc., then align them with the corresponding part of the text. A [Dual Softmax Loss](https://paperswithcode.com/method/dual-softmax-loss) (DSL) is used to avoid the one-way optimum-match which occurs in previous contrastive methods. Introducing the intrinsic prior of each pair in a batch, DSL serves as a reviser to correct the similarity matrix and achieves the dual optimal match.

Source	Large-Scale Learnable Graph Convolutional Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com