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Found 5 papers, 2 papers with code
Continual Learning: Applications and the Road Forward
Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting what was learned in the past.
Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting
Carefully taking both aspects into account, we show that, even though it is true that feature forgetting can be small in absolute terms, newly learned information tends to be forgotten just as catastrophically at the level of the representation as it is at the output level.
CLAD: A realistic Continual Learning benchmark for Autonomous Driving
In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classification and object detection.
Re-examining Distillation For Continual Object Detection
Training models continually to detect and classify objects, from new classes and new domains, remains an open problem.
Rehearsal revealed: The limits and merits of revisiting samples in continual learning
Learning from non-stationary data streams and overcoming catastrophic forgetting still poses a serious challenge for machine learning research.
