A per-class loss reweighting scheme based on distributional robustness allows CLIP models to perform class-incremental and domain-incremental learning with minimal memory while limiting forgetting on CIFAR-100, ImageNet1K, and DomainNet.
Foster: Feature boosting and compression for class-incremental learning
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Memory-Efficient Continual Learning with CLIP Models
A per-class loss reweighting scheme based on distributional robustness allows CLIP models to perform class-incremental and domain-incremental learning with minimal memory while limiting forgetting on CIFAR-100, ImageNet1K, and DomainNet.