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Found 3 papers, 2 papers with code
Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition
However, INER faces the challenge of catastrophic forgetting specific for incremental learning, further aggravated by background shift (i. e., old and future entity types are labeled as the non-entity type in the current task).
Crucial Semantic Classifier-based Adversarial Learning for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA), which aims to explore the transferrable features from a well-labeled source domain to a related unlabeled target domain, has been widely progressed.
No One Left Behind: Real-World Federated Class-Incremental Learning
These issues render global model to undergo catastrophic forgetting on old categories, when local clients receive new categories consecutively under limited memory of storing old categories.
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