Knowledge distillation is a representative technique for model compression and acceleration, which is important for deploying neural networks on resource�...
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In this paper, we first quantitatively define the uniformity of the sampled data for training, providing a unified view for methods that learn from biased data.
Knowledge distillation is a representative technique for model compression and acceleration, which is important for deploying neural networks on resource�...
In this paper, we first quantitatively define the uniformity of the sampled data for training, providing a unified view for methods that learn from biased data.
This paper quantitatively defines the uniformity of the sampled data for training, providing a unified view for methods that learn from biased data and�...
As a result, they achieve less biased results in face verification and perform better than state-of-the-art adversarial debiasing approaches.
Knowledge distillation is widely used as a means of improving the performance of a relatively simple “student” model using the predictions from a complex�...
Apr 3, 2024 � to rectify the incorrect supervision and Data Selection to select appropriate samples for distillation to reduce the impact of ... A bias�...
A teacher model with minimal bias or calibrate its outputs to reduce bias transfer during distillation. Focus on distilling information from teacher�...
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