Varying data augmentation policies and regularization over the course of optimization has led to performance improvements over using fixed values. We show that population based training is a useful tool to continuously search those hyperparameters, within a fixed budget. This greatly simplifies the experimental burden and computational cost of finding such optimal schedules. We experiment in speech recognition by optimizing SpecAugment this way, as well as dropout. It compares favorably to a baseline that does not change those hyperparameters over the course of training, with an 8% relative WER improvement. We obtain 5.18% word error rate on LibriSpeech's test-other.
@article{arxiv.2010.03899,
title = {Population Based Training for Data Augmentation and Regularization in Speech Recognition},
author = {Daniel Haziza and Jérémy Rapin and Gabriel Synnaeve},
journal= {arXiv preprint arXiv:2010.03899},
year = {2020}
}