以任意精度和稀疏度进行神经网络的鲁棒性训练

Chengxi Ye, Grace Chu, Yanfeng Liu, Yichi Zhang, Lukasz Lew, Andrew Howard
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引用次数: 0

摘要

量化和稀疏化固有的不连续操作给反向传播带来了障碍。在超低精度和稀疏状态下训练深度神经网络时,这尤其具有挑战性。我们提出了一种新颖、稳健和通用的解决方案:去噪仿射变换,它能在这些具有挑战性的条件下稳定训练。通过将量化和稀疏化表述为训练过程中的扰动,我们得出了一种基于脊回归的抗扰动方法。我们的解决方案采用片断常数骨干模型来确保性能下限,并具有内在降噪机制来减轻扰动引起的破坏。此外,我们的方法为时空二元神经网络的训练提供了一个新的视角,有助于缩小人工神经网络与生物神经网络之间的差距。
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Robust Training of Neural Networks at Arbitrary Precision and Sparsity
The discontinuous operations inherent in quantization and sparsification introduce obstacles to backpropagation. This is particularly challenging when training deep neural networks in ultra-low precision and sparse regimes. We propose a novel, robust, and universal solution: a denoising affine transform that stabilizes training under these challenging conditions. By formulating quantization and sparsification as perturbations during training, we derive a perturbation-resilient approach based on ridge regression. Our solution employs a piecewise constant backbone model to ensure a performance lower bound and features an inherent noise reduction mechanism to mitigate perturbation-induced corruption. This formulation allows existing models to be trained at arbitrarily low precision and sparsity levels with off-the-shelf recipes. Furthermore, our method provides a novel perspective on training temporal binary neural networks, contributing to ongoing efforts to narrow the gap between artificial and biological neural networks.
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