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 SElf-SupErvised Distillation (SEED), 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 SEED dramatically boosts the performance of small networks on downstream tasks. Compared with self-supervised baselines, SEED improves the top-1 accuracy from 42.2% to 67.6% on EfficientNet-B0 and from 36.3% to 68.2% on MobileNetV3-Large on the ImageNet-1k dataset.
Original language | English (US) |
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State | Published - 2021 |
Event | 9th International Conference on Learning Representations, ICLR 2021 - Virtual, Online Duration: May 3 2021 → May 7 2021 |
Conference
Conference | 9th International Conference on Learning Representations, ICLR 2021 |
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City | Virtual, Online |
Period | 5/3/21 → 5/7/21 |
ASJC Scopus subject areas
- Language and Linguistics
- Computer Science Applications
- Education
- Linguistics and Language