Selective sampling for accelerating training of deep neural networks

Berry Weinstein, Shai Fine, Yacov Hel-Or

Abstract:

We present a selective sampling method designed to accelerate the training of deep neural networks. To this end, we introduce a novel measurement, the {\it minimal margin score} (MMS), which measures the minimal amount of displacement an input should take until its predicted classification is switched. For multi-class linear classification, the MMS measure is a natural generalization of the margin-based selection criterion, which was thoroughly studied in the binary classification setting. In addition, the MMS measure provides an interesting insight into the progress of the training process and can be useful for designing and monitoring new training regimes. Empirically we demonstrate a substantial acceleration when training commonly used deep neural network architectures for popular image classification tasks. The efficiency of our method is compared against the standard training procedures, and against commonly used selective sampling alternatives: Hard negative mining selection, and Entropy-based selection.
Finally, we demonstrate an additional speedup when we adopt a more aggressive learning-drop regime while using the MMS selective sampling method.

Code: https://github.com/paper-submissions/mms-select