Image translation methods typically aim to manipulate a set of labeled attributes (given as supervision at training time e.g. domain label) while leaving the unlabeled attributes intact. Current methods achieve either: (i) disentanglement, which exhibits low visual fidelity and can only be satisfied where the attributes are perfectly uncorrelated. (ii) visually-plausible translations, which are clearly not disentangled. In this work, we propose OverLORD, a single framework for disentangling labeled and unlabeled attributes as well as synthesizing high-fidelity images, which is composed of two stages; (i) Disentanglement: Learning disentangled representations with latent optimization. Differently from previous approaches, we do not rely on adversarial training or any architectural biases. (ii) Synthesis: Training feed-forward encoders for inferring the learned attributes and tuning the generator in an adversarial manner to increase the perceptual quality. When the labeled and unlabeled attributes are correlated, we model an additional representation that accounts for the correlated attributes and improves disentanglement. We highlight that our flexible framework covers multiple image translation settings e.g. attribute manipulation, pose-appearance translation, segmentation-guided synthesis and shape-texture transfer. In an extensive evaluation, we present significantly better disentanglement with higher translation quality and greater output diversity than state-of-the-art methods.

本文提出一种名为OverLORD的单一框架，用于解缠标记和未标记的属性，以及合成高保真度图像，并通过潜在的优化实现分离学习，该方法不依赖对抗性训练或任何结构偏差，并在生成更具感知品质的图片中具有显着更好的解离和更高的输出多样性.

图像翻译中的解缠缚技术扩展