Sequenced-Replacement Sampling for Deep Learning
Abstract
We propose sequenced-replacement sampling (SRS) for training deep neural networks. The basic idea is to assign a fixed sequence index to each sample in the dataset. Once a mini-batch is randomly drawn in each training iteration, we refill the original dataset by successively adding samples according to their sequence index. Thus we carry out replacement sampling but in a batched and sequenced way. In a sense, SRS could be viewed as a way of performing "mini-batch augmentation". It is particularly useful for a task where we have a relatively small images-per-class such as CIFAR-100. Together with a longer period of initial large learning rate, it significantly improves the classification accuracy in CIFAR-100 over the current state-of-the-art results. Our experiments indicate that training deeper networks with SRS is less prone to over-fitting. In the best case, we achieve an error rate as low as 10.10%.
Cite
@article{arxiv.1810.08322,
title = {Sequenced-Replacement Sampling for Deep Learning},
author = {Chiu Man Ho and Dae Hoon Park and Wei Yang and Yi Chang},
journal= {arXiv preprint arXiv:1810.08322},
year = {2018}
}