Combines the channel attention of the widely known [spatial squeeze and channel excitation (SE)](https://paperswithcode.com/method/squeeze-and-excitation-block) block and the spatial attention of the [channel squeeze and spatial excitation (sSE)](https://paperswithcode.com/method/channel-squeeze-and-spatial-excitation#) block to build a spatial and channel attention mechanism for image segmentation tasks.

**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.

CodeT5

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

Concurrent Spatial and Channel Squeeze & Excitation

Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks

**Clipped Double Q-learning** is a variant on [Double Q-learning](https://paperswithcode.com/method/double-q-learning) that upper-bounds the less biased Q estimate $Q\_{\theta\_{2}}$ by the biased estimate $Q\_{\theta\_{1}}$. This is equivalent to taking the minimum of the two estimates, resulting in the following target update:

$$ y\_{1} = r + \gamma\min\_{i=1,2}Q\_{\theta'\_{i}}\left(s', \pi\_{\phi\_{1}}\left(s'\right)\right) $$

The motivation for this extension is that vanilla double [Q-learning](https://paperswithcode.com/method/q-learning) is sometimes ineffective if the target and current networks are too similar, e.g. with a slow-changing policy in an actor-critic framework.

Source	Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com