**DropBlock** is a structured form of [dropout](https://paperswithcode.com/method/dropout) directed at regularizing convolutional networks. In DropBlock, units in a contiguous region of a feature map are dropped together.  As DropBlock discards features in a correlated area, the networks must look elsewhere for evidence to fit the data.

DropBlock

DropBlock: A regularization method for convolutional networks

A **Projection Discriminator** is a type of discriminator for generative adversarial networks. It is motivated by a probabilistic model in which the distribution of the conditional variable $\textbf{y}$ given $\textbf{x}$ is discrete or uni-modal continuous distributions.

If we look at the original solution for the loss function $\mathcal{L}\_{D}$ in the vanilla GANs, we can decompose it into the sum of two log-likelihood ratios:

$$ f^{*}\left(\mathbf{x}, \mathbf{y}\right) = \log\frac{q\left(\mathbf{x}\mid{\mathbf{y}}\right)q\left(\mathbf{y}\right)}{p\left(\mathbf{x}\mid{\mathbf{y}}\right)p\left(\mathbf{y}\right)} = \log\frac{q\left(\mathbf{y}\mid{\mathbf{x}}\right)}{p\left(\mathbf{y}\mid{\mathbf{x}}\right)} + \log\frac{q\left(\mathbf{x}\right)}{p\left(\mathbf{x}\right)}  = r\left(\mathbf{y\mid{x}}\right) + r\left(\mathbf{x}\right) $$

We can model the log likelihood ratio $r\left(\mathbf{y\mid{x}}\right)$ and  $r\left(\mathbf{x}\right)$ by some parametric functions $f\_{1}$ and $f\_{2}$ respectively. If we make a standing assumption that $p\left(y\mid{x}\right)$ and $q\left(y\mid{x}\right)$ are simple distributions like those that are Gaussian or discrete log linear on the feature space, then the parametrization of the following form becomes natural:

$$ f\left(\mathbf{x}, \mathbf{y}; \theta\right) = f\_{1}\left(\mathbf{x}, \mathbf{y}; \theta\right) + f\_{2}\left(\mathbf{x}; \theta\right) = \mathbf{y}^{T}V\phi\left(\mathbf{x}; \theta\_{\phi}\right) + \psi\left(\phi(\mathbf{x}; \theta\_{\phi}); \theta\_{\psi}\right) $$

where $V$ is the embedding matrix of $y$, $\phi\left(·, \theta\_{\phi}\right)$ is a vector output function of $x$, and $\psi\left(·, \theta\_{\psi}\right)$ is a scalar function of the same $\phi\left(\mathbf{x}; \theta\_{\phi}\right)$ that appears in $f\_{1}$. The learned parameters $\theta = ${$V, \theta\_{\phi}, \theta\_{\psi}$} are trained to optimize the adversarial loss. This model of the discriminator is the projection.

Projection Discriminator

cGANs with Projection Discriminator

The Contour Proposal Network (CPN) detects possibly overlapping objects in an image while simultaneously fitting pixel-precise closed object contours. The CPN can incorporate state of the art object detection architectures as backbone networks into a fast single-stage instance segmentation model that can be trained end-to-end.

Contour Proposal Networks for Biomedical Instance Segmentation

**LLaMA** is a collection of foundation language models ranging from 7B to 65B parameters. It is based on the transformer architecture with various improvements that were subsequently proposed. The main difference with the original architecture are listed below.

- RMSNorm normalizing function is used to improve the training stability, by normalizing the input of each transformer sub-layer, instead of normalizing the output.
- The ReLU non-linearity is replaced by the SwiGLU activation function to improve performance.
- Absolute positional embeddings are removed and instead rotary positional embeddings (RoPE) are added at each layer of the network.

LLaMA

LLaMA: Open and Efficient Foundation Language Models

**Spatial Group-wise Enhance** is a module for convolutional neural networks that can adjust the
importance of each sub-feature by generating an attention factor for each spatial location in each semantic group, so that every individual group can autonomously enhance its learnt expression and suppress possible noise

Inside each feature group, we model a spatial enhance mechanism inside each feature group, by scaling the feature vectors over all the locations with an attention mask. This attention mask is designed to suppress the possible noise and highlight the correct semantic feature regions. Different from other popular attention methods, it utilises the similarity between the global statistical feature and the local ones of each location as the source of generation for the attention masks.

Spatial Group-wise Enhance

Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks

**Collaborative Distillation** is a new knowledge distillation method (named Collaborative Distillation) for encoder-decoder based neural style transfer to reduce the number of convolutional filters. The main idea is underpinned by a finding that the encoder-decoder pairs construct an exclusive collaborative relationship, which is regarded as a new kind of knowledge for style transfer models.

Collaborative Distillation

Collaborative Distillation for Ultra-Resolution Universal Style Transfer

A **PixelCNN** is a generative model that uses autoregressive connections to model images pixel by pixel, decomposing the joint image distribution as a product of conditionals. PixelCNNs are much faster to train than [PixelRNNs](https://paperswithcode.com/method/pixelrnn) because convolutions are inherently easier to parallelize; given the vast number of pixels present in large image datasets this is an important advantage.

PixelCNN

Pixel Recurrent Neural Networks

**SNIP**, or **Scale Normalization for Image Pyramids**, is a multi-scale training scheme that selectively back-propagates the gradients of object instances of different sizes as a function of the image scale. SNIP is a modified version of MST where only the object instances that have a resolution close to the pre-training dataset, which is typically 224x224, are used for training the detector. In multi-scale training (MST), each image is observed at different resolutions therefore, at a high resolution (like 1400x2000) large objects are hard to classify and at a low resolution (like 480x800) small objects are hard to classify. Fortunately, each object instance appears at several different scales and some of those appearances fall in the desired scale range. In order to eliminate extreme scale objects, either too large or too small, training is only performed on objects that fall in the desired scale range and the remainder are simply ignored during back-propagation. Effectively, SNIP uses all the object instances during training, which helps capture all the variations in appearance and
pose, while reducing the domain-shift in the scale-space for the pre-trained network.

SNIP

An Analysis of Scale Invariance in Object Detection - SNIP

TAPEX is a conceptually simple and empirically powerful pre-training approach to empower existing models with table reasoning skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesising executable SQL queries.

TAPEX

TAPEX: Table Pre-training via Learning a Neural SQL Executor

**Graph Finite-State Automaton**, or **GFSA**, is a differentiable layer for learning graph structure that adds a new edge type (expressed as a weighted adjacency matrix) to a base graph. This layer can be trained end-to-end to add derived relationships (edges) to arbitrary graph-structured data based on performance on a downstream task.

GFSA

Learning Graph Structure With A Finite-State Automaton Layer

**Content-Aware ReAssembly of FEatures (CARAFE)** is an operator for feature upsampling in convolutional neural networks. CARAFE has several appealing properties: (1) Large field of view. Unlike previous works (e.g. bilinear interpolation) that only exploit subpixel neighborhood, CARAFE can aggregate contextual information within a large receptive field. (2) Content-aware handling. Instead of using a fixed kernel for all samples (e.g. deconvolution), CARAFE enables instance-specific content-aware handling, which generates adaptive kernels on-the-fly. (3) Lightweight and fast to compute.

CARAFE

CARAFE: Content-Aware ReAssembly of FEatures

**Routed Attention** is an attention pattern proposed as part of the [Routing Transformer](https://paperswithcode.com/method/routing-transformer) architecture.  Each attention module
considers a clustering of the space: the current timestep only attends to context belonging to the same cluster. In other word, the current time-step query is routed to a limited number of context through its cluster assignment. This can be contrasted with [strided](https://paperswithcode.com/method/strided-attention) attention patterns and those proposed with the [Sparse Transformer](https://paperswithcode.com/method/sparse-transformer).

In the image to the right, the rows represent the outputs while the columns represent the inputs. The different colors represent cluster memberships for the output token.

Routing Attention

Efficient Content-Based Sparse Attention with Routing Transformers

**AlphaZero** is a reinforcement learning agent for playing board games such as Go, chess, and shogi. 

AlphaZero

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Transformer neural networks have achieved state-of-the-art results for unstructured data such as text and images but their adoption for graph-structured data has been limited. This is partly due to the difficulty of incorporating complex structural information in the basic transformer framework. We propose a simple yet powerful extension to the transformer - residual edge channels. The resultant framework, which we call Edge-augmented Graph Transformer (EGT), can directly accept, process and output structural information as well as node information. It allows us to use global self-attention, the key element of transformers, directly for graphs and comes with the benefit of long-range interaction among nodes. Moreover, the edge channels allow the structural information to evolve from layer to layer, and prediction tasks on edges/links can be performed directly from the output embeddings of these channels. In addition, we introduce a generalized positional encoding scheme for graphs based on Singular Value Decomposition which can improve the performance of EGT. Our framework, which relies on global node feature aggregation, achieves better performance compared to Convolutional/Message-Passing Graph Neural Networks, which rely on local feature aggregation within a neighborhood. We verify the performance of EGT in a supervised learning setting on a wide range of experiments on benchmark datasets. Our findings indicate that convolutional aggregation is not an essential inductive bias for graphs and global self-attention can serve as a flexible and adaptive alternative.

Global Self-Attention as a Replacement for Graph Convolution

**Charformer** is a type of [Transformer](https://paperswithcode.com/methods/category/transformers) model that learns a subword tokenization end-to-end as part of the model. Specifically it uses [GBST](https://paperswithcode.com/method/gradient-based-subword-tokenization) that automatically learns latent subword representations from characters in a data-driven fashion. Following GBST, the soft subword sequence is passed through [Transformer](https://paperswithcode.com/method/transformer) layers.

Charformer

Charformer: Fast Character Transformers via Gradient-based Subword Tokenization

The **Linear Combination of Activations**, or **LinComb**, is a type of activation function that has trainable parameters and uses the linear combination of other activation functions.

$$LinComb(x) = \sum\limits_{i=0}^{n} w_i \mathcal{F}_i(x)$$

LinComb

Trainable Activations for Image Classification

**BinaryBERT** is a [BERT](https://paperswithcode.com/method/bert)-variant that applies quantization in the form of weight binarization. Specifically, ternary weight splitting is proposed which initializes BinaryBERT by equivalently splitting from a half-sized ternary network. To obtain BinaryBERT, we first train a half-sized [ternary BERT](https://paperswithcode.com/method/ternarybert) model, and then apply a [ternary weight splitting](https://paperswithcode.com/method/ternary-weight-splitting) operator to obtain the latent full-precision and quantized weights as the initialization of the full-sized BinaryBERT. We then fine-tune BinaryBERT for further refinement.

BinaryBERT

BinaryBERT: Pushing the Limit of BERT Quantization

**Reversible Residual Blocks** are skip-connection blocks that learn *reversible* residual functions with reference to the layer inputs. It is proposed as part of the [RevNet](https://paperswithcode.com/method/revnet) CNN architecture. 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 by 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](https://paperswithcode.com/method/resnet):

$$y\_{1} = x\_{1} + F\left(x\_{2}\right)$$
$$y\_{2} = x\_{2} + G\left(y\_{1}\right)$$

Each layer’s activations can be reconstructed from the next layer’s activations as follows:

$$ x\_{2} = y\_{2} − G\left(y\_{1}\right)$$
$$ x\_{1} = y\_{1} − F\left(x\_{2}\right)$$

AI Glossary

DropBlock

Projection Discriminator

Contour Proposal Network

LLaMA

Spatial Group-wise Enhance

Collaborative Distillation

PixelCNN

SNIP

Table Pre-training via Execution

Graph Finite-State Automaton

CARAFE

Routing Attention

AlphaZero

Edge-augmented Graph Transformer

Charformer

Linear Combination of Activations

BinaryBERT

Reversible Residual Block