Diffusion models generate samples by gradually
removing noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).

**Patch AutoAugment** is a patch-level automatic data augmentation algorithm that automatically searches for the optimal augmentation policies for the patches of an image. Specifically, PAA allows each patch DA operation to be controlled by an agent and models it as a Multi-Agent Reinforcement Learning (MARL) problem. At each step, PAA samples the most effective operation for each patch based on its content and the semantics of the whole image. The agents cooperate as a team and share a unified team reward for achieving the joint optimal DA policy of the whole image. PAA is co-trained with a target network through adversarial training. At each step, the policy network samples the most effective operation for each patch based on its content and the semantics of the image.

Local Patch AutoAugment with Multi-Agent Collaboration

Diffusion

Denoising Diffusion Probabilistic Models

**T2T-ViT** (Tokens-To-Token Vision Transformer) is a type of [Vision Transformer](https://paperswithcode.com/method/vision-transformer) which incorporates 1) a layerwise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision [transformer](https://paperswithcode.com/method/transformer) motivated by CNN architecture design after empirical study.

Source	Denoising Diffusion Probabilistic Models
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com