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Efficient Model Adaptation for Continual Learning at the Edge
This paper presents the Encoder-Adaptor-Reconfigurator (EAR) framework for efficient continual learning under domain shifts.
Scenarios: A New Representation for Complex Scene Understanding
The ability for computational agents to reason about the high-level content of real world scene images is important for many applications.
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