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Layer-Specific Adaptation with Prototype-Anchored Adapter-Space Transport (LSA-PAST) is proposed, a parameter-efficient method tailored to composition-IL that achieves state-of-the-art accuracy-forgetting trade-off performance for composition-IL.
The continual evolution of malware variants necessitates detection systems that can adapt to new threats without retraining from scratch. However, continually updating models on new data often leads to catastrophic forgetting, where previously learned knowledge is overwritten. While continual learning has been increasi...
In collaborative foundation model fine-tuning, client data is rarely homogeneous. Instead, clients typically possess unknown mixtures of distinct data distributions, or tasks. Conventional federated learning primarily addresses heterogeneity across clients without explicitly resolving latent task mixtures within each c...
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