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Training neural networks with large batch is of fundamental significance todeep learning. Large batch training remarkably reduces the amount of trainingtime but has difficulties in maintaining accuracy. Recent works have putforward optimization methods such as LARS and LAMB to tackle this issue throughadaptive layer-wise optimization using trust ratios. Though prevailing, suchmethods are observed to still suffer from unstable and extreme trust ratioswhich degrades performance. In this paper, we propose a new variant of LAMB,called LAMBC, which employs trust ratio clipping to stabilize its magnitude andprevent extreme values. We conducted experiments on image classification taskssuch as ImageNet and CIFAR-10 and our empirical results demonstrate promisingimprovements across different batch sizes.

Improving Layer-wise Adaptive Rate Methods using Trust Ratio Clipping