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Random Reconstructed Unpaired Image-to-Image Translation | IEEE Journals & Magazine | IEEE Xplore
Source: ieeexplore.ieee.org
Article summary:
1. The goal of unpaired image-to-image translation is to learn a mapping from a source domain to a target domain without using any labeled examples of paired images.
2. Existing unpaired image-to-image translation algorithms generate untruthful images which are over-colored and lack details.
3. The proposed random reconstructed unpaired image-to-image translation (RRUIT) framework uses random reconstruction to preserve the high-level features in the source and adopts an adversarial strategy to learn the distribution in the target.
Article analysis:
The article is generally reliable, as it provides detailed information about the proposed RRUIT framework and its advantages over existing methods. The authors provide evidence for their claims by conducting extensive experiments on photorealistic stylization and artistic stylization, which demonstrate the superiority of their proposed method. However, there are some potential biases that should be noted. For example, the authors do not explore any counterarguments or alternative approaches to solving this problem, nor do they discuss any possible risks associated with their approach. Additionally, they do not present both sides of the argument equally; instead, they focus solely on promoting their own method without considering other options or perspectives. Finally, there is no discussion of how this approach could be applied in real world scenarios or what implications it may have for future research in this field.