It is common to use the softmax cross-entropy loss to train neural networks on classification datasets where a single class label is assigned to each example. However, it has been shown that modifying softmax cross-entropy with label smoothing or regularizers such as dropout can lead to higher performance. This paper studies a variety of loss functions and output layer regularization strategies on image classification tasks. We observe meaningful differences in model predictions, accuracy, calibration, and out-of-distribution robustness for networks trained with different objectives. However, differences in hidden representations of networks trained with different objectives are restricted to the last few layers; representational similarity reveals no differences among network layers that are not close to the output. We show that all objectives that improve over vanilla softmax loss produce greater class separation in the penultimate layer of the network, which potentially accounts for improved performance on the original task, but results in features that transfer worse to other tasks.

本文探讨了在图像分类任务中使用多种损失函数和正则化器去提高测试精度，但是并不清楚这些损失函数是否可以学习出更好的下游任务表示。作者研究了训练目标的选择如何影响在ImageNet上训练的卷积神经网络的隐藏表示的可转移性，结果表明选择不同的损失函数会导致不同的结果，进一步指出了在为原始任务学习不变特征和为转移任务学习相关特征之间存在一种折衷。

更好的损失函数为何会导致特征较差的迁移性？