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Inspired by the success of Self-Supervised Learning (SSL) in learning visual representations from unlabeled data, a few recent works have studied SSL in the context of Continual Learning (CL), where multiple tasks are learned sequentially, giving rise to a new paradigm, namely Self-Supervised Continual Learning (SSCL). It has been shown that the SSCL outperforms Supervised Continual Learning (SCL) as the learned representations are more informative and robust to catastrophic forgetting. However, building upon the training process of SSL, prior SSCL studies involve training all the parameters for each task, resulting to prohibitively high training cost. In this work, we first analyze the training time and memory consumption and reveals that the backward gradient calculation is the bottleneck. Moreover, by investigating the task correlations in SSCL, we further discover an interesting phenomenon that, with the SSL-learned background model, the intermediate features are highly correlated between tasks. Based on these new finding, we propose a new SSCL method with layer-wise freezing which progressively freezes partial layers with the highest correlation ratios for each task to improve training computation efficiency and memory efficiency. Extensive experiments across multiple datasets are performed, where our proposed method shows superior performance against the SoTA SSCL methods under various SSL frameworks. For example, compared to LUMP, our method achieves 1.18x, 1.15x, and 1.2x GPU training time reduction, 1.65x, 1.61x, and 1.6x memory reduction, 1.46x, 1.44x, and 1.46x backward FLOPs reduction, and 1.31%/1.98%/1.21% forgetting reduction without accuracy degradation on three datasets, respectively.
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SparCL: Sparse Continual Learning on the Edge
Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, i.e., model performance deterioration on past tasks when learning a new task. However, the training efficiency of a CL system is under-investigated, which limits the real-world application of CL systems under resource-limited scenarios. In this work, we propose a novel framework called Sparse Continual Learning(SparCL), which is the first study that leverages sparsity to enable cost-effective continual learning on edge devices. SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity, data efficiency, and gradient sparsity. Specifically, we propose task-aware dynamic masking (TDM) to learn a sparse network throughout the entire CL process, dynamic data removal (DDR) to remove less informative training data, and dynamic gradient masking (DGM) to sparsify the gradient updates. Each of them not only improves efficiency, but also further mitigates catastrophic forgetting. SparCL consistently improves the training efficiency of existing state-of-the-art (SOTA) CL methods by at most 23X less training FLOPs, and, surprisingly, further improves the SOTA accuracy by at most 1.7%. SparCL also outperforms competitive baselines obtained from adapting SOTA sparse training methods to the CL setting in both efficiency and accuracy. We also evaluate the effectiveness of SparCL on a real mobile phone, further indicating the practical potential of our method.
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- PAR ID:
- 10417491
- Date Published:
- Journal Name:
- 2022 Conference on Neural Information Processing Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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