**CodeT5** is a [Transformer](https://paperswithcode.com/methods/category/transformers)-based model for code understanding and generation based on the [T5 architecture](https://paperswithcode.com/method/t5). It utilizes an identifier-aware pre-training objective that considers the crucial token type information (identifiers) from code. Specifically, the denoising [Seq2Seq](https://paperswithcode.com/method/seq2seq) objective of T5 is extended with two identifier tagging and prediction tasks to enable the model to better leverage the token type information from programming languages, which are the identifiers assigned by developers. To improve the natural language-programming language alignment, a bimodal dual learning objective is used for a bidirectional conversion between natural language and programming language.

The **Shifted Rectified Linear Unit**, or **ShiLU**, is a modification of **[ReLU](https://paperswithcode.com/method/relu)** activation function that has trainable parameters.

$$ShiLU(x) = \alpha ReLU(x) + \beta$$

ShiLU

Trainable Activations for Image Classification

CodeT5

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

DecomCAM decomposes intermediate activation maps into orthogonal features using singular value decomposition and generates saliency maps by integrating them.

Source	CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com