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Continual Classification Learning Using Generative Models
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abstract
Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences. Neural networks can learn multiple tasks when trained on them jointly, but cannot maintain performance on previously learned tasks when tasks are presented one at a time. This problem is called catastrophic forgetting. In this work, we propose a classification model that learns continuously from sequentially observed tasks, while preventing catastrophic forgetting. We build on the lifelong generative capabilities of [10] and extend it to the classification setting by deriving a new variational bound on the joint log likelihood, $\log p(x; y)$.
Forward citations
Cited by 2 Pith papers
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LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning
Adapting a text-to-image generator with task-specific LoRA adapters and filtering samples by the model's own confidence improves synthetic replay in continual vision-language learning.
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LADA: Scalable Label-Specific CLIP Adapter for Continual Learning
LADA adds lightweight label-specific memory vectors to a frozen CLIP image encoder, removing the need for task-parameter selection and reporting state-of-the-art X-TAIL benchmark results.
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