CVPR 2023 – Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation

In this episode we discuss Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation
by Gaurav Patel, Konda Reddy Mopuri, Qiang Qiu. The paper introduces a framework called Learning to Retain while Acquiring, which addresses the issue of non-stationary distribution of pseudo-samples in the Adversarial Data-free Knowledge Distillation (DFKD) framework. The proposed method treats the tasks of learning from newly generated samples and retaining knowledge on previously met samples as meta-train and meta-test, respectively. The authors also identify an implicit aligning factor between the two tasks, showing that the student update strategy enforces a common gradient direction for both objectives. The effectiveness of the proposed method is demonstrated through extensive evaluation and comparison on multiple datasets.