**Inception-v3 Module** is an image block used in the [Inception-v3](https://paperswithcode.com/method/inception-v3) architecture. This architecture is used on the coarsest (8 × 8) grids to promote high dimensional representations.

**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.

Dense Contrastive Learning

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

Inception-v3 Module

Rethinking the Inception Architecture for Computer Vision

**Weighted Recurrent Quality Enhancement**, or **WRQE**, is a recurrent quality enhancement network for video compression that takes both compressed frames and the bit stream as inputs. In the recurrent cell of WRQE, the memory and update signal are weighted by quality features to reasonably leverage multi-frame information for enhancement.

Source	Rethinking the Inception Architecture for Computer Vision
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com