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Title: Total Style Transfer with a Single Feed-Forward Network

Paper content:
Recent image style transferring methods achieved arbitrary stylization with input content and style images.
To transfer the style of an arbitrary image to a content image, these methods used a feed-forward network with a lowest-scaled feature transformer or a cascade of the networks with a feature transformer of a corresponding scale.
However, their approaches did not consider either multi-scaled style in their single-scale feature transformer or dependency between the transformed feature statistics across the cascade networks.
This shortcoming resulted in generating partially and inexactly transferred style in the generated images.
To overcome this limitation of partial style transfer, we propose a total style transferring method which transfers multi-scaled feature statistics through a single feed-forward process.
First, our method transforms multi-scaled feature maps of a content image into those of a target style image by considering both inter-channel correlations in each single scaled feature map and inter-scale correlations between multi-scaled feature maps.
Second, each transformed feature map is inserted into the decoder layer of the corresponding scale using skip-connection.
Finally, the skip-connected multi-scaled feature maps are decoded into a stylized image through our trained decoder network.
Recent image style transferring methodsJohnson et al. (2016) ; BID18 improved image generating speed up to sub-realtime processing by learning a feed-forward network of a single style or several fixed stylesDumoulin et al. (2017) .
Huang et al.Huang & Belongie (2017) proposed an adaptive instance normalization layer (AdaIN) that adaptively transforms the statistics of an encoded content feature into that of a target style feature and they achieved style transferring into arbitrary input target style.
However, they did not consider multi-scaled style characteristics of an imageGatys et al. (2016) but only a single scale feature in differentiating styles inside AdaIN layer.
Li et al.Li et al. (2017b) proposed to use cascade networks that cumulatively transfer the multi-scaled style characteristics by using a network per scale as shown in FIG0
(a).
They also transformed correlation between channels of feature map by using their whitening and coloring transformer (WCT).
However, their cascade scheme requires multiple feed-forward passes to produce a stylized image and it is not guaranteed that the transferred style through a network is preserved after going through the subsequent networks because of inter-scale dependency in the multi-scaled styles of an image.
Therefore, transferring multi-scaled style without interference between scales is still remained to study.In this paper, we propose an improved feed-forward network structure ( FIG0 ) and a multi-scaled style transferring method, called total style transfer, to efficiently perform style transfer in all scales of feature maps through a single feed-forward pass.
Our work has the following contributions.•
Transforming both intra-scale and inter-scale statistics of multi-scaled feature map: There exist both of inter and intra-correlations in the encoded multi-scaled feature map as shown in fig.2 (
b).
Therefore, we match the second-order statistics, i.e., mean and covariance, of the encoded multi-scaled feature map considering the correlations not only between channels in each scale (intra-scale correlation) but also between scales (inter-scale correlation).
Our feature transformer makes the transformed feature map closer to the target style feature map and this results in an output style closer to the target style.
Figure 2: Correlation between channels in the multi-scaled feature map of the input image
(a) extracted from the pre-trained VGG16 BID16 .
The area corresponding to each scale of feature map is divided into red lines.
In case of intra-scale feature transform, the diagonal rectangles on the correlation matrix are used.
In case of inter-scale feature transform, entire region of the correlation matrix is considered.•
Decoder learning with multi-scaled style loss: we use a multi-scaled style loss consistent to the feature transformer, i.e., mean and covariance loss between the concatenated feature map ( FIG1 ). Using
our multi-scaled style loss allows the decoder network to generate an output image of co-occurring multi-scale patterns which is better style expression than independently occurring scale patterns on the image that the existing methods generated.• Multi-scaled
style transfer with a single feed-forward network: we use skip-connections for each decoder layer as shown in FIG0 (b) to consider
the transformed feature map as well as the decoded feature map. By doing this,
the style of scale corresponding to the layer and the transferred multi-scaled style so far are optimally merged into the next layer. Therefore, our
method transfers multi-scaled style through a feed-forward pass in a single network instead of multiple feed-forward passes of cascade networks ( FIG0 ) without considering inter-scale correlation.In the remained of this paper, we review previous works closely related to this work in Sec. 2, our multi-scaled style transforming method is described in Sec. 3, the effectiveness of our method is tested and proven by a bundle of experiments in Sec. 4, and this work is concluded in Sec. 5.
In this paper, we proposed a total style transfer network that generates an image through a single feed-forward network by utilizing multi-scale features of content and style images.
Our intra-scale feature transformer transfers multi-scale style characteristics of the target style image and our inter-scale feature transformer transfers even more style characteristics of inter-scale correlation into the content image.
By using our intra/inter scale feature transform, our total style transfer network achieved the lowest style loss among the existing feed-forward network methods.
In addition, we modified the feed-forward network structure by using skip-connections which make our decoder network to utilize all transformed multi-scale features.
This modification allowed a single feed-forward network to generate image of multi-scaled style without using multiple feedforward networks of cascade scheme, and resulted in the reduced test time by 31% and memory consumption by 4% compared to cascade network scheme.

assistant: text: A paper suggesting a method to transform the style of images using deep neural networks.