Stochastic Weight Averaging in Parallel: Large-Batch Training that Generalizes Well
In collaboration with University of California, Berkeley
AuthorsVipul Gupta, Santiago Akle Serrano, Dennis DeCoste
We propose Stochastic Weight Averaging in Parallel (SWAP), an algorithm to accelerate DNN training. Our algorithm uses large mini-batches to compute an approximate solution quickly and then refines it by averaging the weights of multiple models computed independently and in parallel. The resulting models generalize equally well as those trained with small mini-batches but are produced in a substantially shorter time. We demonstrate the reduction in training time and the good generalization performance of the resulting models on the computer vision datasets CIFAR10, CIFAR100, and ImageNet.
Apple sponsored the 8th International Conference on Learning Representations (ICLR) in April 2020, which took place virtually from April 26 - May 1. ICLR focuses on the advancement of representation learning, and this year’s conference included presentations on cutting-edge research on deep learning areas including computer vision, text understanding, data science, and more.