AUTO SEG-LOSS: SEARCHING METRIC SURROGATES FOR SEMANTIC SEGMENTATION

Abstract

Designing proper loss functions is essential in training deep networks. Especially in the field of semantic segmentation, various evaluation metrics have been proposed for diverse scenarios. Despite the success of the widely adopted crossentropy loss and its variants, the mis-alignment between the loss functions and evaluation metrics degrades the network performance. Meanwhile, manually designing loss functions for each specific metric requires expertise and significant manpower. In this paper, we propose to automate the design of metric-specific loss functions by searching differentiable surrogate losses for each metric. We substitute the non-differentiable operations in the metrics with parameterized functions, and conduct parameter search to optimize the shape of loss surfaces. Two constraints are introduced to regularize the search space and make the search efficient. Extensive experiments on PASCAL VOC and Cityscapes demonstrate that the searched surrogate losses outperform the manually designed loss functions consistently. The searched losses can generalize well to other datasets and networks.

1. INTRODUCTION

Loss functions are of indispensable components in training deep networks, as they drive the feature learning process for various applications with specific evaluation metrics. However, most metrics, like the commonly used 0-1 classification error, are non-differentiable in their original forms and cannot be directly optimized via gradient-based methods. Empirically, the cross-entropy loss serves well as an effective surrogate objective function for a variety of tasks concerning categorization. This phenomenon is especially prevailing in image semantic segmentation, where various evaluation metrics have been designed to address the diverse task focusing on different scenarios. Some metrics measure the accuracy on the whole image, while others focus more on the segmentation boundaries. Although cross-entropy and its variants work well for many metrics, the mis-alignment between network training and evaluation still exist and inevitably leads to performance degradation. Typically, there are two ways for designing metric-specific loss functions in semantic segmentation. The first is to modify the standard cross-entropy loss to meet the target metric (Ronneberger et al., 2015; Wu et al., 2016) . The other is to design other clever surrogate losses for specific evaluation metrics (Rahman & Wang, 2016; Milletari et al., 2016) . Despite the improvements, these handcrafted losses need expertise and are non-trivial to extend to other evaluation metrics. In contrast to designing loss functions manually, an alternative approach is to find a framework that can design proper loss functions for different evaluation metrics in an automated manner, motivated by recent progress in AutoML (Zoph & Le, 2017; Pham et al., 2018; Liu et al., 2018; Li et al., 2019) . Although automating the design process for loss functions is attractive, it is non-trivial to apply an

availability

://github.com/fundamentalvision/ Auto

