Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning

By Do-Yeon Kim, Dong-Jun Han, Jun Seo, Jaekyun Moon. 2023

75 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. Keon-Hee Park
2. Kyungwoo Song
3. Gyeong-Moon Park
4. Xin Ning
5. Weijun Li
6. Prayag Tiwari
7. Hang Ran
8. Lusi Li

Citations in the corpus

• Pre-trained Vision and Language Transformers are Few-Shot Incremental Learners (2024, methodology): “In detail, selectively freezing parameters (WaRP [12]) leads to severe forgetting during incremental sessions.”
• A Survey on Few-Shot Class-Incremental Learning (2023, methodology): “From the perspective of parameter feature space, Kim et al. (2023) proposed WaRP by fusing the advantages of F2M (SHI et al., 2021) for finding flat minimums of the loss function and FSLL (Mazumder et al., 2021) for parameter fine-tuning.”