Abstract: Deep low-level networks are successful in laboratory benchmarks, but still suffer from severe generalization problems in real-world applications, especially for the deraining task. An ``acknowledgement'' of deep learning drives us to use the training data with higher complexity, expecting the network to learn richer knowledge to overcome generalization problems. Through extensive systematic experiments, we show that this approach fails to improve their generalization ability but instead makes the networks overfit to degradations even more. Our experiments establish that it is capable of training a deraining network with better generalization by reducing the training data complexity. Because the networks are slacking off during training, i.e. learn the less complex element in the image content and degradation to reduce the training loss. When the background image is less complex than the rain streak, the network will focus on the reconstruction of the background without overfitting the rain patterns, thus achieving a good generalization effect. Our research demonstrates excellent application potential and provides an indispensable perspective and research methodology for understanding the generalization problem of low-level vision.
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