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Unpaired Image-to-Image Translation Using Adversarial Consistency Loss

European Conference on Computer Vision (ECCV)CCF B

Hyperplane Lab

Cited 154|Views168
Abstract
Unpaired image-to-image translation is a class of vision problems whose goal is to find the mapping between different image domains using unpaired training data. Cycle-consistency loss is a widely used constraint for such problems. However, due to the strict pixel-level constraint, it cannot perform geometric changes, remove large objects, or ignore irrelevant texture. In this paper, we propose a novel adversarial-consistency loss for image-to-image translation. This loss does not require the translated image to be translated back to be a specific source image but can encourage the translated images to retain important features of the source images and overcome the drawbacks of cycle-consistency loss noted above. Our method achieves state-of-the-art results on three challenging tasks: glasses removal, male-to-female translation, and selfie-to-anime translation.
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Image Inpainting,Image Synthesis,Texture Synthesis,Unsupervised Learning
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要点】:本文提出了一种新的对抗性一致性损失函数,用于无配对图像到图像的转换任务,克服了传统循环一致性损失的局限,实现了在多个转换任务中的最佳性能。

方法】:通过引入对抗性一致性损失,该方法使转换后的图像能够保留源图像的重要特征,同时避免了循环一致性损失在形状变化、移除大物体或忽略无关纹理方面的限制。

实验】:在眼镜移除、男性到女性的转换以及自拍照到动漫风格的转换三个挑战性任务中,使用对抗性一致性损失函数的模型达到了当前最佳效果,具体数据集名称未在摘要中提及。