The paper proposes an unsupervised domain alignment method using GANs with cycle consistency, adversarial, and SSIM losses to augment training data and reduce low-level dataset biases in computer vision.
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Unsupervised Domain Alignment to Mitigate Low Level Dataset Biases
The paper proposes an unsupervised domain alignment method using GANs with cycle consistency, adversarial, and SSIM losses to augment training data and reduce low-level dataset biases in computer vision.