Accelerating Deep Neural Network Training for Action Recognition on a Cluster of GPUs

Abstract

Due to the additional temporal dimension, large-scale video action recognition is even more challenging than image recognition and typically takes days to train on modern GPUs even for modest-sized datasets. We propose algorithms and techniques to accelerate training of deep neural networks for action recognition on a cluster of GPUs. In terms of convergence and scaling, our distributed training algorithm with adaptive batch size is provably superior to popular asynchronous stochastic gradient descent algorithms. The convergence analysis of our algorithm shows it is possible to reduce communication cost and at the same time minimize the number of iterations needed for convergence. We customize the Adam optimizer for our distributed algorithm to improve efficiency. In addition, we employ transfer-learning to further reduce training time while improving validation accuracy. Compared with the base-line single-GPU stochastic gradient descent implementation of the two-stream training approach, our implementation achieves super-linear speedups on 16 GPUs while improving validation accuracy. For the UCFI0l and HMDB51 datasets, the validation accuracies achieved are 93.1 % and 67.9% respectively. As far as we know, these are the highest accuracies achieved with the two-stream approach that does not involve computationally expensive 3D convolutions or pretraining on much larger datasets.