Learning forward-compatible and domain-invariant representations for cross-domain few-shot class-incremental learning.
Saved in:
| Title: | Learning forward-compatible and domain-invariant representations for cross-domain few-shot class-incremental learning. |
|---|---|
| Authors: | Shi W; School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China. Electronic address: 24120371@bjtu.edu.cn., Yan X; School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China. Electronic address: xud_yan@bjtu.edu.cn., Yuan J; Research Center for Language Intelligence of China, Capital Normal University, Beijing, China. Electronic address: jzyuan@cnu.edu.cn., Lu H; School of Mathematics and Statistics, Shandong University, Weihai, Shandong, China. Electronic address: lhwh@sdu.edu.cn., Feng S; Tangshan Research Institute, Beijing Jiaotong University, Tangshan, China. Electronic address: shfeng@bjtu.edu.cn. |
| Source: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 May 04; Vol. 202, pp. 109070. Date of Electronic Publication: 2026 May 04. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1879-2782 |
|---|---|
| DOI: | 10.1016/j.neunet.2026.109070 |
