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Found 4 papers, 4 papers with code
Cross-Domain Few-Shot Learning via Adaptive Transformer Networks
Most few-shot learning works rely on the same domain assumption between the base and the target tasks, hindering their practical applications.
Dynamic Long-Term Time-Series Forecasting via Meta Transformer Networks
A slow learner tailors suitable representations to fast learners.
Few-Shot Continual Learning via Flat-to-Wide Approaches
This paper proposes a few-shot continual learning approach, termed FLat-tO-WidE AppRoach (FLOWER), where a flat-to-wide learning process finding the flat-wide minima is proposed to address the catastrophic forgetting problem.
Assessor-Guided Learning for Continual Environments
This paper proposes an assessor-guided learning strategy for continual learning where an assessor guides the learning process of a base learner by controlling the direction and pace of the learning process thus allowing an efficient learning of new environments while protecting against the catastrophic interference problem.
