Modern neural architectures for classification tasks are trained using the cross-entropy loss, which is believed to be empirically superior to the square loss. In this work we provide evidence indicating that this belief may not be well-founded. We explore several major neural architectures and a range of standard benchmark datasets for NLP, automatic speech recognition (ASR) and computer vision tasks to show that these architectures, with the same hyper-parameter settings as reported in the literature, perform comparably or better when trained with the square loss, even after equalizing computational resources. Indeed, we observe that the square loss produces better results in the dominant majority of NLP and ASR experiments. Cross-entropy appears to have a slight edge on computer vision tasks. We argue that there is little compelling empirical or theoretical evidence indicating a clear-cut advantage to the cross-entropy loss. Indeed, in our experiments, performance on nearly all non-vision tasks can be improved, sometimes significantly, by switching to the square loss. We posit that training using the square loss for classification needs to be a part of best practices of modern deep learning on equal footing with cross-entropy.

本文提供证据表明，对于NLP和ASR任务，使用方块损失函数进行神经网络训练，可以获得比交叉熵更好的效果，并且可以提高计算资源的利用率。我们认为，使用方块损失函数需要成为现代深度学习最佳实践的一部分，与交叉熵在平等基础上竞争。

神经网络分类任务中使用平方误差与交叉熵训练的结构评估