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arXiv · 2205.10490

Aligning Logits Generatively for Principled Black-Box Knowledge Distillation

Jing Ma·Xiang Xiang·Ke Wang·Yuchuan Wu·Yongbin Li
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Abstract

Black-Box Knowledge Distillation (B2KD) is a formulated problem for cloud-to-edge model compression with invisible data and models hosted on the server. B2KD faces challenges such as limited Internet exchange and edge-cloud disparity of data distributions. In this paper, we formalize a two-step workflow consisting of deprivatization and distillation, and theoretically provide a new optimization direction from logits to cell boundary different from direct logits alignment. With its guidance, we propose a new method Mapping-Emulation KD (MEKD) that distills a black-box cumbersome model into a lightweight one. Our method does not differentiate between treating soft or hard responses, and consists of: 1) deprivatization: emulating the inverse mapping of the teacher function with a generator, and 2) distillation: aligning low-dimensional logits of the teacher and student models by reducing the distance of high-dimensional image points. For different teacher-student pairs, our method yields inspiring distillation performance on various benchmarks, and outperforms the previous state-of-the-art approaches.

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Jing Ma, Xiang Xiang, Ke Wang, Yuchuan Wu, Yongbin Li. 2024-03-30. Aligning Logits Generatively for Principled Black-Box Knowledge Distillation. https://arxiv.org/abs/2205.10490

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