Deep deraining networks, while successful in laboratory benchmarks, consistently encounter substantial generalization issues when deployed in real-world applications. A prevailing perspective in deep learning encourages the use of highly complex training data, with the expectation that a richer image content knowledge will facilitate overcoming the generalization problem. However, through comprehensive and systematic experimentation, we discovered that this strategy does not enhance the generalization capability of these networks. On the contrary, it exacerbates the tendency of networks to overfit to specific degradations. Our experiments reveal that better generalization in a deraining network can be achieved by simplifying the complexity of the training data. This is due to the networks are slacking off during training, that is, learning the least complex elements in the image content and degradation to minimize training loss. When the complexity of the background image is less than that of the rain streaks, the network will prioritize the reconstruction of the background, thereby avoiding overfitting to the rain patterns and resulting in improved generalization performance. Our research not only offers a valuable perspective and methodology for better understanding the generalization problem in low-level vision tasks, but also displays promising practical potential.

翻译：深度去雨网络在实验室基准测试中表现出色，但在实际应用部署时却持续面临严重的泛化问题。深度学习领域的主流观点倾向于使用高度复杂的训练数据，期望更丰富的图像内容知识有助于克服泛化问题。然而，通过全面系统的实验，我们发现这一策略并不能提升网络的泛化能力，反而加剧了网络对特定退化的过拟合倾向。实验表明，通过简化训练数据的复杂度，反而能实现去雨网络更好的泛化性能。这是由于网络在训练过程中存在"偷懒"行为——即优先学习图像内容和退化中最简单的元素以最小化训练损失。当背景图像的复杂度低于雨条纹时，网络会优先重构背景，从而避免对雨纹模式的过拟合，最终获得更优的泛化性能。本研究不仅为理解低级视觉任务中的泛化问题提供了有价值的视角与方法，还展现出良好的实际应用潜力。