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Title: Slimmed Asymmetrical Contrastive Learning and Cross Distillation for Lightweight Model Training
Contrastive learning (CL) has been widely investigated with various learning mech- anisms and achieves strong capability in learning representations of data in a self-supervised manner using unlabeled data. A common fashion of contrastive learning on this line is employing large-sized encoders to achieve comparable performance as the supervised learning counterpart. Despite the success of the labelless training, current contrastive learning algorithms failed to achieve good performance with lightweight (compact) models, e.g., MobileNet, while the re- quirements of the heavy encoders impede the energy-efficient computation, espe- cially for resource-constrained AI applications. Motivated by this, we propose a new self-supervised CL scheme, named SACL-XD, consisting of two technical components, Slimmed Asymmetrical Contrastive Learning (SACL) and Cross- Distillation (XD), which collectively enable efficient CL with compact models. While relevant prior works employed a strong pre-trained model as the teacher of unsupervised knowledge distillation to a lightweight encoder, our proposed method trains CL models from scratch and outperforms them even without such an expensive requirement. Compared to the SoTA lightweight CL training (dis- tillation) algorithms, SACL-XD achieves 1.79% ImageNet-1K accuracy improve- ment on MobileNet-V3 with 64⇥ training FLOPs reduction. Code is available at  more » « less
Award ID(s):
2144751 2314591 2328803 2342726
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
NeurIPS 2023
Date Published:
Journal Name:
Thirty-seventh Conference on Neural Information Processing Systems
Medium: X
Sponsoring Org:
National Science Foundation
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