**AEDA**, or **An Easier Data Augmentation**, is a type of data augmentation technique for text classification which includes only the insertion of various punctuation marks into the input sequence. AEDA preserves all the input information and does not mislead the network since it keeps the word order intact while changing their positions in that the words are shifted to the right.

A **SqueezeNeXt Block** is a two-stage bottleneck module used in the [SqueezeNeXt](https://paperswithcode.com/method/squeezenext) architecture to reduce the number of input channels to the 3 × 3 [convolution](https://paperswithcode.com/method/convolution). We decompose with separable convolutions to further reduce the number of parameters (orange parts), followed by a 1 × 1 expansion module.

SqueezeNeXt Block

SqueezeNext: Hardware-Aware Neural Network Design

AEDA

AEDA: An Easier Data Augmentation Technique for Text Classification

**Dense Contrastive Learning** is a self-supervised learning method for dense prediction tasks. It implements self-supervised learning by optimizing a pairwise contrastive (dis)similarity loss at the pixel level between two views of input images. Contrasting with regular contrastive loss, the contrastive loss is computed between the single feature vectors outputted by the global projection head, at the level of global feature, while the dense contrastive loss is computed between the dense feature vectors outputted by the dense projection head, at the level of local feature.

Source	AEDA: An Easier Data Augmentation Technique for Text Classification
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com