Learning without Forgetting
When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.
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Class Incremental LearningContinual LearningDisjoint 10-1Disjoint 15-1Disjoint 15-5Domain 1-1Domain 11-1Domain 11-5Incremental LearningOverlapped 10-1Overlapped 15-1Overlapped 15-5Similar Papers 제목 키워드 기반
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