SEED: Self-supervised Distillation For Visual RepresentationDownload PDF

Published: 12 Jan 2021, Last Modified: 03 Apr 2024ICLR 2021 PosterReaders: Everyone
Keywords: Self Supervised Learning, Knowledge Distillation, Representation Learning
Abstract: This paper is concerned with self-supervised learning for small models. The problem is motivated by our empirical studies that while the widely used contrastive self-supervised learning method has shown great progress on large model training, it does not work well for small models. To address this problem, we propose a new learning paradigm, named $\textbf{SE}$lf-Sup$\textbf{E}$rvised $\textbf{D}$istillation (${\large S}$EED), where we leverage a larger network (as Teacher) to transfer its representational knowledge into a smaller architecture (as Student) in a self-supervised fashion. Instead of directly learning from unlabeled data, we train a student encoder to mimic the similarity score distribution inferred by a teacher over a set of instances. We show that ${\large S}$EED dramatically boosts the performance of small networks on downstream tasks. Compared with self-supervised baselines, ${\large S}$EED improves the top-1 accuracy from 42.2% to 67.6% on EfficientNet-B0 and from 36.3% to 68.2% on MobileNet-v3-Large on the ImageNet-1k dataset.
One-sentence Summary: We propose ${\large S}$EED, a self-supervised distillation technique for visual representation learning.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Code: [![github](/images/github_icon.svg) jacobswan1/SEED](
Data: [CIFAR-10](, [CIFAR-100](, [ImageNet](, [MS COCO](
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](
7 Replies