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Found 3 papers, 3 papers with code
Weighted Ensemble Models Are Strong Continual Learners
In this work, we study the problem of continual learning (CL) where the goal is to learn a model on a sequence of tasks, such that the data from the previous tasks becomes unavailable while learning on the current task data.
Mini but Mighty: Finetuning ViTs with Mini Adapters
Vision Transformers (ViTs) have become one of the dominant architectures in computer vision, and pre-trained ViT models are commonly adapted to new tasks via fine-tuning.
Rethinking Class-incremental Learning in the Era of Large Pre-trained Models via Test-Time Adaptation
However, repeated fine-tuning on each task destroys the rich representations of the PTMs and further leads to forgetting previous tasks.
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