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FedFed: Feature Distillation against Data Heterogeneity in Federated Learning Zhiqin Y ang
Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data heterogeneity, yet incurs a dilemma in preserving privacy and promoting
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SKFlow: Learning Optical Flow with Super Kernels Shangkun Sun 1 Y uanqi Chen 1 Y u Zhu
Optical flow estimation is a classical yet challenging task in computer vision.
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