Title: Improving the Latent Space of Image Style Transfer. Abstract: Existing neural style transfer studies utilize statistical information of features from a pre-trained encoder as representations of the style and achieve 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. The style distance between some images of different styles is small 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. The training scheme can be directly added to existing style transfer methods and significantly improve their results. Extensive experimental results demonstrate the effectiveness and superiority of our methods. 