Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting

TL;DR

This paper argues that saturating output activations like softmax hinder learning, and demonstrates that using linear activations with exponential gradient boosting accelerates convergence and enhances performance in classification tasks.

Contribution

The paper reveals that normalization in softmax is detrimental and introduces exponential gradient boosting with linear outputs as a superior alternative for classification.

Findings

Faster convergence on CIFAR-10 and ImageNet

Improved performance in semantic segmentation

33% reduction in training time with the new method

Abstract

In this work, we show that saturating output activation functions, such as the softmax, impede learning on a number of standard classification tasks. Moreover, we present results showing that the utility of softmax does not stem from the normalization, as some have speculated. In fact, the normalization makes things worse. Rather, the advantage is in the exponentiation of error gradients. This exponential gradient boosting is shown to speed up convergence and improve generalization. To this end, we demonstrate faster convergence and better performance on diverse classification tasks: image classification using CIFAR-10 and ImageNet, and semantic segmentation using PASCAL VOC 2012. In the latter case, using the state-of-the-art neural network architecture, the model converged 33% faster with our method (roughly two days of training less) than with the standard softmax activation, and with a slightly better performance to boot.