Retro: Reusing teacher projection head for efficient embedding distillation on Lightweight Models via Self-supervised Learning
<|reference_start|>Retro: Reusing teacher projection head for efficient embedding distillation on Lightweight Models via Self-supervised Learning: 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.<|reference_end|>
@article{nguyen2024retro:,
    title={Retro: Reusing teacher projection head for efficient embedding
  distillation on Lightweight Models via Self-supervised Learning},
    author={Khanh-Binh Nguyen and Chae Jung Park},
    journal={arXiv preprint arXiv:2405.15311},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15311},
    primaryClass={cs.CV cs.AI}
}