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cs.LG2026
Continual Distillation of Teachers from Different Domains
Nicolas Michel, Maorong Wang, Jiangpeng He +1
Deep learning models continue to scale, with some requiring more storage than many large-scale datasets. Thus, we introduce a new paradigm: Continual Distillation (CD), where a stu…
cs.CV2026
Dual-Imbalance Continual Learning for Real-World Food Recognition
Xiaoyan Zhang, Jiangpeng He
Visual food recognition in real-world dietary logging scenarios naturally exhibits severe data imbalance, where a small number of food categories appear frequently while many other…
cs.CV2026
One Adapter for All: Towards Unified Representation in Step-Imbalanced Class-Incremental Learning
Xiaoyan Zhang, Jiangpeng He
Class-incremental learning (CIL) aims to acquire new classes over time while retaining prior knowledge, yet most setups and methods assume balanced task streams. In practice, the n…