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Found 3 papers, 3 papers with code
When Prompt-based Incremental Learning Does Not Meet Strong Pretraining
However, existing prompt-based methods heavily rely on strong pretraining (typically trained on ImageNet-21k), and we find that their models could be trapped if the potential gap between the pretraining task and unknown future tasks is large.
DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition
In this work, we explore effective Vision Transformers to pursue a preferable trade-off between the computational complexity and size of the attended receptive field.
Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data
The diverse generated samples could effectively prevent DNN from forgetting when learning new tasks.
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