Search Results for author:
Found 4 papers, 3 papers with code
MetaMix: Towards Corruption-Robust Continual Learning With Temporally Self-Adaptive Data Transformation
To make them trustworthy and robust to corruptions deployed in safety-critical scenarios, we propose a meta-learning framework of self-adaptive data augmentation to tackle the corruption robustness in CL.
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions
Two key challenges arise in this more realistic setting: (i) how to use unlabeled data in the presence of a large amount of unlabeled out-of-distribution (OOD) data; and (ii) how to prevent catastrophic forgetting on previously learned task distributions due to the task distribution shift.
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution
To address these problems, for the first time, we propose a principled memory evolution framework to dynamically evolve the memory data distribution by making the memory buffer gradually harder to be memorized with distributionally robust optimization (DRO).
Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness
In this paper, we explore a more practical and challenging setting where task distribution changes over time with domain shift.
