**Bilateral Guided Aggregation Layer** is a feature fusion layer for semantic segmentation that aims to enhance mutual connections and fuse different types of feature representation. It was used in the [BiSeNet V2](https://paperswithcode.com/method/bisenet-v2) architecture. Specifically, within the BiSeNet implementation, the layer was used to employ the contextual information of the Semantic Branch to guide the feature response of Detail Branch. With different scale guidance, different scale feature representations can be captured, which inherently encodes the multi-scale information.

A **Reversible Residual Network**, or **RevNet**, is a variant of a [ResNet](https://paperswithcode.com/method/resnet) where each layer’s activations can be reconstructed exactly from the next layer’s. Therefore, the activations for most layers need not be stored in memory during backpropagation. The result is a network architecture whose activation storage requirements are independent of depth, and typically at least an order of magnitude smaller compared with equally sized ResNets.

RevNets are composed of a series of reversible blocks. Units in each layer are partitioned into two groups, denoted $x\_{1}$ and $x\_{2}$; the authors find what works best is partitioning the channels. Each reversible block takes inputs $\left(x\_{1}, x\_{2}\right)$ and produces outputs $\left(y\_{1}, y\_{2}\right)$ according to the following additive coupling rules – inspired the transformation in [NICE](https://paperswithcode.com/method/nice) (nonlinear independent components estimation) – and residual functions $F$ and $G$ analogous to those in standard ResNets:

$$y\_{1} = x\_{1} + F\left(x\_{2}\right)$$
$$y\_{2} = x\_{2} + G\left(y\_{1}\right)$$

Each layer’s activations can be reconstructed from the next layer’s activations as follows:

$$ x\_{2} = y\_{2} − G\left(y\_{1}\right)$$
$$ x\_{1} = y\_{1} − F\left(x\_{2}\right)$$

Note that unlike residual blocks, reversible blocks must have a stride of 1 because otherwise the layer
discards information, and therefore cannot be reversible. Standard ResNet architectures typically
have a handful of layers with a larger stride. If we define a RevNet architecture analogously, the
activations must be stored explicitly for all non-reversible layers.

RevNet

The Reversible Residual Network: Backpropagation Without Storing Activations

Bilateral Guided Aggregation Layer

BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation

The **Levenshtein Transformer** (LevT) is a type of [transformer](https://paperswithcode.com/method/transformer) that aims to address the lack of flexibility of previous decoding models. Notably, in previous frameworks, the length of generated sequences is either fixed or monotonically increased as the decoding proceeds. The authors argue this is incompatible with human-level intelligence where humans can revise, replace, revoke or delete any part of their generated text. Hence, LevT is proposed to bridge this gap by breaking the in-so-far standardized decoding mechanism and replacing it with two basic operations — insertion and deletion.

LevT is trained using imitation learning. The resulted model contains two policies and they are executed in an alternate manner. The authors argue that with this model decoding becomes more flexible. For example, when the decoder is given an empty token, it falls back to a normal sequence generation model. On the other hand, the decoder acts as a refinement model when the initial state is a low-quality generated sequence.

One crucial component in LevT framework is the learning algorithm. The authors leverage the characteristics of insertion and deletion — they are complementary but also adversarial. The algorithm they propose is called “dual policy learning”. The idea is that when training one policy (insertion or deletion), we use the output from its adversary at the previous iteration as input. An expert policy, on the other hand, is drawn to provide a correction signal.

Source	BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com