103 citations · 278 across the 43 of their papers we have counts for
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cs.CV2022
Federated Selective Aggregation for Knowledge Amalgamation
Donglin Xie, Ruonan Yu, Gongfan Fang +5
In this paper, we explore a new knowledge-amalgamation problem, termed Federated Selective Aggregation (FedSA). The goal of FedSA is to train a student model for a new task with th…
cs.CL2022★ 2 cited
Prompting to Distill: Boosting Data-Free Knowledge Distillation via Reinforced Prompt
Xinyin Ma, Xinchao Wang, Gongfan Fang +2
Data-free knowledge distillation (DFKD) conducts knowledge distillation via eliminating the dependence of original training data, and has recently achieved impressive results in ac…
cs.CV2022★ 18 cited
Knowledge Amalgamation for Object Detection with Transformers
Haofei Zhang, Feng Mao, Mengqi Xue +4
Knowledge amalgamation (KA) is a novel deep model reusing task aiming to transfer knowledge from several well-trained teachers to a multi-talented and compact student. Currently, m…