A **Gated Convolutional Network** is a type of language model that combines convolutional networks with a gating mechanism. Zero padding is used to ensure future context can not be seen. Gated convolutional layers can be stacked on top of other hierarchically. Model predictions are then obtained with an [adaptive softmax](https://paperswithcode.com/method/adaptive-softmax) layer.

**Chained-Tracker**, or **CTracker**,  is an online model for multiple-object tracking. It chains paired bounding boxes regression results estimated from overlapping nodes, of which each node covers two adjacent frames. The paired regression is made attentive by object-attention (brought by a detection module) and identity-attention (ensured by an ID verification module).

The joint attention module guides the paired boxes regression branch to focus on informative spatial regions with two other branches. One is the object classification branch, which predicts the confidence scores for the first box in the detected box pairs, and such scores are used to guide the regression branch to focus on the foreground regions. The other one is the ID verification branch whose prediction facilitates the regression branch to focus on regions corresponding to the same target. Finally, the bounding box pairs are filtered according to the classification confidence. Then, the generated box pairs belonging to the adjacent frame pairs could be associated using simple methods like IoU (Intersection over Union) matching according to their boxes in the common frame. In this way, the tracking process could be achieved by chaining all the adjacent frame pairs (i.e. chain nodes) sequentially.

CTracker

Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking

Gated Convolution Network

Language Modeling with Gated Convolutional Networks

**BigGAN-deep** is a deeper version (4x) of [BigGAN](https://paperswithcode.com/method/biggan).  The main difference is a slightly differently designed [residual block](https://paperswithcode.com/method/residual-block). Here the $z$ vector is concatenated with the conditional vector without splitting it into chunks.  It is also based on residual blocks with bottlenecks. BigGAN-deep uses a different strategy than BigGAN aimed at preserving identity throughout the skip connections. In G, where the number of channels needs to be reduced, BigGAN-deep simply retains the first group of channels and drop the rest to produce the required number of channels. In D, where the number of channels should be increased, BigGAN-deep passes the input channels unperturbed, and concatenates them with the remaining channels produced by a 1 × 1 [convolution](https://paperswithcode.com/method/convolution). As far as the
network configuration is concerned, the discriminator is an exact reflection of the generator. 

There are two blocks at each resolution (BigGAN uses one), and as a result BigGAN-deep is four times
deeper than BigGAN. Despite their increased depth, the BigGAN-deep models have significantly
fewer parameters mainly due to the bottleneck structure of their residual blocks.

Source	Language Modeling with Gated Convolutional Networks
Year	2000
Data Source	Papers with Code archive (CC BY-SA)