DDGR: Continual Learning with Deep Diffusion-based Generative Replay

By Rui Gao, Weiwei Liu. 2023

113 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Da-Wei Zhou
2. Han-Jia Ye
3. De-chuan Zhan
4. Qiwen Wang
5. Zhiyuan Qi
6. Ziwei Liu
7. Junsu Kim
8. Hoseong Cho

Citations in the corpus

• Recent Advances of Continual Learning in Computer Vision: An Overview (2021, neutral): “Wang et al. [136] focused on utilizing unlabelled data instances and proposed Continual Replay with Discriminator Consistency.”
• SDDGR: Stable Diffusion-Based Deep Generative Replay for Class Incremental Object Detection (2024, methodology): “Among these, Replay methods are most frequently utilized for their simple yet powerful effects and can be categorized into two types: partial experience replay (ER) [2, 4, 14, 27, 44, 47] and generative replay (GR) [6, 12, 23, 58, 66, 67].”
• Class-Incremental Learning: A Survey (2023, background): “Similarly, DDGR [52] adopts a diffusion model as the generator and calculates an instruction operator through the classifier to instruct the generation of samples.”