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Found 3 papers, 2 papers with code
Parameter-Level Soft-Masking for Continual Learning
Although several techniques have achieved learning with no CF, they attain it by letting each task monopolize a sub-network in a shared network, which seriously limits knowledge transfer (KT) and causes over-consumption of the network capacity, i. e., as more tasks are learned, the performance deteriorates.
Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain Recommendations
To address this data sparsity problem, cross-domain recommender systems (CDRSs) exploit the data from an auxiliary source domain to facilitate the recommendation on the sparse target domain.
Partially Relaxed Masks for Lightweight Knowledge Transfer without Forgetting in Continual Learning
The goal of this work is to endow such systems with the additional ability to transfer knowledge among tasks when the tasks are similar and have shared knowledge to achieve higher accuracy.
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