Unifying Synergies between Self-supervised Learning and Dynamic Computation

• URL: http://arxiv.org/abs/2301.09164v3
• Date: Sat, 9 Sep 2023 20:43:13 GMT
• Title: Unifying Synergies between Self-supervised Learning and Dynamic Computation
• Authors: Tarun Krishna, Ayush K Rai, Alexandru Drimbarean, Eric Arazo, Paul Albert, Alan F Smeaton, Kevin McGuinness, Noel E O'Connor
• Abstract summary: We present a novel perspective on the interplay between SSL and DC paradigms. We show that it is feasible to simultaneously learn a dense and gated sub-network from scratch in a SSL setting. The co-evolution during pre-training of both dense and gated encoder offers a good accuracy-efficiency trade-off.
• Score: 53.66628188936682
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computationally expensive training strategies make self-supervised learning (SSL) impractical for resource constrained industrial settings. Techniques like knowledge distillation (KD), dynamic computation (DC), and pruning are often used to obtain a lightweightmodel, which usually involves multiple epochs of fine-tuning (or distilling steps) of a large pre-trained model, making it more computationally challenging. In this work we present a novel perspective on the interplay between SSL and DC paradigms. In particular, we show that it is feasible to simultaneously learn a dense and gated sub-network from scratch in a SSL setting without any additional fine-tuning or pruning steps. The co-evolution during pre-training of both dense and gated encoder offers a good accuracy-efficiency trade-off and therefore yields a generic and multi-purpose architecture for application specific industrial settings. Extensive experiments on several image classification benchmarks including CIFAR-10/100, STL-10 and ImageNet-100, demonstrate that the proposed training strategy provides a dense and corresponding gated sub-network that achieves on-par performance compared with the vanilla self-supervised setting, but at a significant reduction in computation in terms of FLOPs, under a range of target budgets (td ).

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