Improving the Latent Space of Image Style Transfer

TL;DR

This paper introduces contrastive training schemes to refine the encoder in neural style transfer, improving style consistency and content detail preservation, leading to better stylization results.

Contribution

It proposes two contrastive training schemes to enhance the latent space for style transfer, addressing style inconsistency and content leak issues.

Findings

Improved style similarity in stylized images.

Enhanced retention of content details.

Significant performance gains over existing methods.

Abstract

Existing neural style transfer researches have studied to match statistical information between the deep features of content and style images, which were extracted by a pre-trained VGG, and achieved significant improvement in synthesizing artistic images. However, in some cases, the feature statistics from the pre-trained encoder may not be consistent with the visual style we perceived. For example, the style distance between images of different styles is less than that of the same style. In such an inappropriate latent space, the objective function of the existing methods will be optimized in the wrong direction, resulting in bad stylization results. In addition, the lack of content details in the features extracted by the pre-trained encoder also leads to the content leak problem. In order to solve these issues in the latent space used by style transfer, we propose two contrastive training schemes to get a refined encoder that is more suitable for this task. The style contrastive loss pulls the stylized result closer to the same visual style image and pushes it away from the content image. The content contrastive loss enables the encoder to retain more available details. We can directly add our training scheme to some existing style transfer methods and significantly improve their results. Extensive experimental results demonstrate the effectiveness and superiority of our methods.