4 papers
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…
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…
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…
From Offline to Online Memory-Free and Task-Free Continual Learning via Fine-Grained Hypergradients
Nicolas Michel, Maorong Wang, Jiangpeng He +1
Continual Learning (CL) aims to learn from a non-stationary data stream where the underlying distribution changes over time. While recent advances have produced efficient memory-fr…