Studies on benign overfitting provide insights for the success of overparameterized deep learning models. In this work, we examine the benign overfitting phenomena in real-world settings. We found that for tasks such as training a ResNet model on ImageNet dataset, the model does not fit benignly. To understand why benign overfitting fails in the ImageNet experiment, we analyze previous benign overfitting models under a more restrictive setup where the number of parameters is not significantly larger than the number of data points. Under this mild overparameterization setup, our analysis identifies a phase change: unlike in the heavy overparameterization setting, benign overfitting can now fail in the presence of label noise. Our study explains our empirical observations, and naturally leads to a simple technique known as self-training that can boost the model's generalization performances. Furthermore, our work highlights the importance of understanding implicit bias in underfitting regimes as a future direction.

研究表明温和过拟合现象对过度参数深度学习模型的成功提供了深刻见解。本文探讨了现实世界中的温和过拟合现象，并发现对于在ImageNet数据集上训练ResNet模型等任务，模型不会温和拟合。在这个较轻的过参数化设置下，我们的分析确定了一种新现象：在标签噪声存在的情况下，温和过拟合可能会失败。此外，我们的工作强调了理解欠拟合环境中的隐式偏差作为未来方向的重要性。

现实中的深度学习可能不容易适应