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Found 3 papers, 3 papers with code
Few-Shot Class Incremental Learning via Robust Transformer Approach
This problem remains an open problem because all recent works are built upon the convolutional neural networks performing sub-optimally compared to the transformer approaches.
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.
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