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arXiv · 2405.15311

Retro: Reusing teacher projection head for efficient embedding distillation on Lightweight Models via Self-supervised Learning

Khanh-Binh Nguyen·Chae Jung Park
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Abstract

Self-supervised learning (SSL) is gaining attention for its ability to learn effective representations with large amounts of unlabeled data. Lightweight models can be distilled from larger self-supervised pre-trained models using contrastive and consistency constraints. Still, the different sizes of the projection heads make it challenging for students to mimic the teacher's embedding accurately. We propose \textsc{Retro}, which reuses the teacher's projection head for students, and our experimental results demonstrate significant improvements over the state-of-the-art on all lightweight models. For instance, when training EfficientNet-B0 using ResNet-50/101/152 as teachers, our approach improves the linear result on ImageNet to $66.9\%$, $69.3\%$, and $69.8\%$, respectively, with significantly fewer parameters.

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Khanh-Binh Nguyen, Chae Jung Park. 2024-05-24. Retro: Reusing teacher projection head for efficient embedding distillation on Lightweight Models via Self-supervised Learning. https://arxiv.org/abs/2405.15311

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