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Real-world applications require the classification model to adapt to new classes without forgetting old ones.
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Overcoming catastrophic forgetting by incremental moment matching
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Bert: Pre-training of deep bidirectional transformers for language understanding
Gan memory with no forgetting
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Supervised contrastive learning
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What do neural networks learn when trained with random labels?
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Semantic drift compensation for class-incremental learning
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Dytox: Transformers for continual learning with dynamic token expansion
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Gradient-based editing of memory examples for online task-free continual learning
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Adapting bert for continual learning of a sequence of aspect sentiment classification tasks
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Preserving earlier knowledge in continual learning with the help of all previous feature extractors
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Rf-badge: Vital sign-based authentication via rfid tag array on badges
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Der: Dynamically expandable representation for class incremental learning
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Looking back on learned experiences for class/task incremental learning
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