Image De-raining via Continual Learning

By Man Zhou, Jie Xiao, Yifan Chang, Xueyang Fu, Aiping Liu, Jin-shan Pan, Zhengjun Zha. 2021

66 citations recorded by the source corpus.

While deep convolutional neural networks (CNNs) have achieved great success on image de-raining task, most existing methods can only learn fixed mapping rules between paired rainy/clean images on a single dataset. This limits their applications in practical situations with multiple and incremental datasets where the mapping rules may change for different types of rain streaks. However, the catastrophic forgetting of traditional deep CNN model challenges the design of generalized framework for multiple and incremental datasets. A strategy of sharing the network structure but in-dependently updating and storing the network parameters on each dataset has been developed as a potential solution. Nevertheless, this strategy is not applicable to compact systems as it dramatically increases the overall training time and parameter space. To alleviate such limitation, in this study, we propose a parameter importance guided weights modification approach, named PIGWM. Specifically, with new dataset (e.g. new rain dataset), the well-trained network weights are updated according to their importance evaluated on previous training dataset. With extensive experimental validation, we demonstrate that a single network with a single parameter set of our proposed method can process multiple rain datasets almost without performance degradation. The proposed model is capable of achieving superior performance on both inhomogeneous and incremental datasets, and is promising for highly compact systems to gradually learn myriad regularities of the different types of rain streaks. The results indicate that our proposed method has great potential for other computer vision tasks with dynamic learning environments.

Subfields: Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality, Meta & Continual Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

  1. René Vidal
  2. Paris V. Giampouras
  3. Liangzu Peng
  4. Ehsan Elhamifar
  5. Paolo Favaro
  6. Shuicheng Yan
  7. Zhouchen Lin
  8. Jitendra Malik

In Meta & Continual Learning

  1. René Vidal
  2. Paris V. Giampouras
  3. Liangzu Peng

Citations in the corpus