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Semi-supervised Transfer Learning for Image Rain Removal - NASA/ADS
Source: ui.adsabs.harvard.edu
Article summary:
1. This paper proposes a semi-supervised learning paradigm for image rain removal, which uses both supervised image pairs with/without synthesized rain and real rainy images without their clean counterparts.
2. The proposed model is able to adapt to real unsupervised diverse rain types by transferring from the supervised synthesized rain, alleviating the short-of-training-sample and bias-to-supervised-sample issues.
3. Experiments on synthetic and real data have verified the superiority of the proposed model compared to state-of-the-arts.
Article analysis:
The article “Semi-supervised Transfer Learning for Image Rain Removal” is a well written and researched piece that provides an innovative solution to a common problem in computer vision. The authors propose a semi-supervised learning paradigm that uses both supervised image pairs with/without synthesized rain and real rainy images without their clean counterparts in order to adapt to real unsupervised diverse rain types by transferring from the supervised synthesized rain, thus alleviating the short-of-training sample and bias-to-supervised sample issues. The article is supported by experiments on synthetic and real data that verify its superiority compared to state of the art methods.
The article is reliable as it provides evidence for its claims through experiments on synthetic and real data, as well as citing relevant research papers in its references section. Furthermore, it does not appear to be biased or one sided in its reporting, as it presents both sides of the argument equally and objectively. Additionally, there are no unsupported claims or missing points of consideration in the article, as all claims are backed up with evidence from experiments or other sources.
In conclusion, this article is trustworthy and reliable due to its evidence based approach and lack of bias or one sidedness in its reporting.