Towards Better De-raining Generalization via Rainy Characteristics Memorization and Replay Article Swipe
Kunyu Wang
,
Xueyang Fu
,
Chengzhi Cao
,
Chengjie Ge
,
Wei Zhai
,
Zheng-Jun Zha
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2506.02477
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2506.02477
Current image de-raining methods primarily learn from a limited dataset, leading to inadequate performance in varied real-world rainy conditions. To tackle this, we introduce a new framework that enables networks to progressively expand their de-raining knowledge base by tapping into a growing pool of datasets, significantly boosting their adaptability. Drawing inspiration from the human brain's ability to continuously absorb and generalize from ongoing experiences, our approach borrow the mechanism of the complementary learning system. Specifically, we first deploy Generative Adversarial Networks (GANs) to capture and retain the unique features of new data, mirroring the hippocampus's role in learning and memory. Then, the de-raining network is trained with both existing and GAN-synthesized data, mimicking the process of hippocampal replay and interleaved learning. Furthermore, we employ knowledge distillation with the replayed data to replicate the synergy between the neocortex's activity patterns triggered by hippocampal replays and the pre-existing neocortical knowledge. This comprehensive framework empowers the de-raining network to amass knowledge from various datasets, continually enhancing its performance on previously unseen rainy scenes. Our testing on three benchmark de-raining networks confirms the framework's effectiveness. It not only facilitates continuous knowledge accumulation across six datasets but also surpasses state-of-the-art methods in generalizing to new real-world scenarios.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2506.02477
- https://arxiv.org/pdf/2506.02477
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4415132965
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https://openalex.org/W4415132965Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.2506.02477Digital Object Identifier
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Towards Better De-raining Generalization via Rainy Characteristics Memorization and ReplayWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-06-03Full publication date if available
- Authors
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Kunyu Wang, Xueyang Fu, Chengzhi Cao, Chengjie Ge, Wei Zhai, Zheng-Jun ZhaList of authors in order
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https://arxiv.org/abs/2506.02477Publisher landing page
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https://arxiv.org/pdf/2506.02477Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2506.02477Direct OA link when available
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0Total citation count in OpenAlex
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