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Publications of SPCL

E. Hoffer, T. Ben-Nun, I. Hubara, N. Giladi, T. Hoefler, D. Soudry:
	Increasing batch size through instance repetition improves generalization (In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2020) Abstract Large-batch SGD is important for scaling training of deep neural networks. However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered. We propose to use batch augmentation: replicating instances of samples within the same batch with different data augmentations. Batch augmentation acts as a regularizer and an accelerator, increasing both generalization and performance scaling for a fixed budget of optimization steps. We analyze the effect of batch augmentation on gradient variance and show that it empirically improves convergence for a wide variety of networks and datasets. Our results show that batch augmentation reduces the number of necessary SGD updates to achieve the same accuracy as the state-of-the-art. Overall, this simple yet effective method enables faster training and better generalization by allowing more computational resources to be used concurrently. Documents access preprint on arxiv:
	BibTeX
@inproceedings{,   author={Elad Hoffer and Tal Ben-Nun and Itay Hubara and Niv Giladi and Torsten Hoefler and Daniel Soudry},   title={{Increasing batch size through instance repetition improves generalization}},   year={2020},   month={06},   booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, }