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Found 3 papers, 1 papers with code
ARUBA: An Architecture-Agnostic Balanced Loss for Aerial Object Detection
In this paper, we denote size of an object as the number of pixels it covers in an image and size imbalance as the over-representation of certain sizes of objects in a dataset.
Proto2Proto: Can you recognize the car, the way I do?
With growing use cases of model reuse and distillation, there is a need to also study transfer of interpretability from one model to another.
Beyond Classification: Knowledge Distillation using Multi-Object Impressions
Knowledge Distillation (KD) utilizes training data as a transfer set to transfer knowledge from a complex network (Teacher) to a smaller network (Student).
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