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Deep Collective Knowledge Distillation
We propose deep collective knowledge distillation for model compression, called DCKD, which is a method for training student models with rich information to acquire knowledge from not only their teacher model but also other student models.
DUET: Detection Utilizing Enhancement for Text in Scanned or Captured Documents
Moreover, ablations are conducted and the results confirm the effectiveness of the synthetic data, auxiliary task, and weak-supervision.
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