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 https://github.com/mengjian0502/SACL-XD.
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AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters
Reducing the model redundancy is an important task to deploy complex deep
learning models to resource-limited or time-sensitive devices. Directly regularizing
or modifying weight values makes pruning procedure less robust and sensitive
to the choice of hyperparameters, and it also requires prior knowledge to tune
different hyperparameters for different models. To build a better generalized and
easy-to-use pruning method, we propose AutoPrune, which prunes the network
through optimizing a set of trainable auxiliary parameters instead of original
weights. The instability and noise during training on auxiliary parameters will not
directly affect weight values, which makes pruning process more robust to noise
and less sensitive to hyperparameters. Moreover, we design gradient update rules
for auxiliary parameters to keep them consistent with pruning tasks. Our method
can automatically eliminate network redundancy with recoverability, relieving
the complicated prior knowledge required to design thresholding functions, and
reducing the time for trial and error. We evaluate our method with LeNet and VGGlike
on MNIST and CIFAR-10 datasets, and with AlexNet, ResNet and MobileNet
on ImageNet to establish the scalability of our work. Results show that our model
achieves state-of-the-art sparsity, e.g. 7%, 23% FLOPs and 310x, 75x compression
ratio for LeNet5 and VGG-like structure without accuracy drop, and 200M and
100M FLOPs for MobileNet V2 with accuracy 73.32% and 66.83% respectively.
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- NSF-PAR ID:
- 10181832
- Date Published:
- Journal Name:
- Advances in neural information processing systems
- Volume:
- 32
- ISSN:
- 1049-5258
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
