Title: TOTAL STYLE TRANSFER WITH A SINGLE FEED-FORWARD NETWORK

Abstract: 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 multiscaled 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 interchannel 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.

1. INTRODUCTION:

Recent image style transferring methodsJohnson et al. (2016) ; Ulyanov et al. (2016; 2017) 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 fig. 1 (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 (fig. 1 (b) ) 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. • 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 (fig. 3 (a) ). 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 fig. 1 (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 (fig. 1 (a) ) 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.

2. RELATED WORK:

Gatys et al. Gatys et al. (2016) represented content and style features of an image using a deep feature map, i.e., the filtered responses of a learned convolutional neural network (CNN). To stylize an input image, they performed pixel-wise optimization of the image to reduce the feature losses of 

3. METHOD:



3.1. MULTI-SCALE FEATURE TRANSFORM:

As described in Gatys et al. (2016) , each scaled feature of CNN represents different style characteristics of an image. So, we utilize multiple feature transformers for each scale feature to transfer total style characteristics of an image. In this section, we explain two schemes of our total style transfer, i.e, intra-scale and inter-scale feature transform, with a single feed-forward network.

3.1.1. INTRA-SCALE FEATURE TRANSFORM:

Our intra-scale feature transform is a set of independent single-scale style transforms as an extended multi-scale version of the single-scale correlation alignment of CORAL Sun et al. (2016) or WCT Li et al. (2017b) .

Suppose we have i-th scale features F c,i ∈ R Ci×(Hc,i×Wc,i) of content image and F s,i ∈ R Ci×(Hs,i×Ws,i) of style image, where C i , H c,i (or H s,i ), and W c,i (or W s,i ) represent the number of channels, spatial height, and width of i-th scale features respectively. For a single-scale style transform with these features, CORAL or WCT performs (1) style normalization and (2) stylization sequentally. In the style normalization step, first zero-centered feature Fc,i ∈ R Ci×(Hc,i×Wc,i) of the content feature F c,i is calculated and then the content feature F c,i is normalized into Fc,i by using its own covariance matrix cov( Fc,i ) ∈ R Ci×Ci as eq.1.

EQUATION

In the stylization step, the normalized content feature Fc,i is stylized into F cs,i by using the square root of covariance matrix cov( Fs,i ) ∈ R Ci×Ci of zero-centered style feature Fs,i and spatial mean µ s,i ∈ R Ci×1 of the style feature F s,i as eq.2.

F cs,i = cov( Fs,i ) Hs,i×Ws,i) .

1 2 • Fc,i + µ s,i • 1 1×(

(2)

Our intra-scale transform method applies the above single-scale transform independently to each scaled feature for i = 1..3 corresponding to {relu_1_2, relu_2_2, relu_3_3} layers. Then, those transformed features F cs,i , i = 1..3 are inserted into the subsequent decoder through skip-connenction. More detail about skip-connection will be described in Sec.3.1.3.

3.1.2. INTER-SCALE FEATURE TRANSFORM:

As shown in fig. 2 (b), there exists not only inter-channel correlation in a certain scale feature but also inter-scale correlation between multi-scale features. These correlations should be considered in order to transfer total style characteristics of an image. CORAL Sun et al. (2016) or WCT Li et al. (2017b) did not consider inter-scale correlation but only inter-channel correlation. Therefore, we propose inter-scale feature transformer which considers more style characteristics of image for style transfer.

To perform feature transform considering both inter-channel and inter-scale correlations, we apply the intra-scale feature transform of Sec.3.1.1 to the concatenated feature F c ∈ R i Ci×(Hc,1×Wc,1) of content image and F s ∈ R i Ci×(Hs,1×Ws,1) of style image (eq.3) instead of independently applying to each scaled features F c,i and F s,i of Sec.3.1.1.

EQUATION

As shown in fig. 3 (a), the content features F c,i and style features F s,i for i = 1..3 are spatially upsampled into U (F c,i ) and U (F s,i ) of a common size (we use the largest size of F c,1 or F s,1 corresponding to {relu_1_2}) and concatenated into F c and F s respectively along the channel axis. After going through a transformer, the transformed feature F cs is split and downsampled into F cs,i ∈ R Ci×(Hc,i×Wc,i) of the original feature size as shown fig. 3 (b) and eq.4,

EQUATION

where, D i (f ) is a function which spatially downsamples f into H c,i × W c,i . These features are inserted into the subsequent decoder through skip-connenction of Sec.3.1.3. Our method is different from the previous cascade scheme of Li et al. (2017b) because we use a single encoder/decoder network, parallel transformers for each scale feature, and merges multi-scaled styles optimally while the cascade scheme needs several encoder/decoder networks (one network per scale feature) and sequentially transfers scaled styles from large to small scale at the risk of degradation in previously transferred scale of style. Avatar-Net Sheng et al. ( 2018) also used a single decoder like ours but it sequentially applied feature transformers from large to small scale without considering possible degradation of the previously transferred scale.

3.2. MULTI-SCALED STYLE LOSS:

We need an appropriate objective function for decoder network to generate a stylized image from the transformed feature map. Among the existing losses such as Gram Gatys et al. (2016) In case of using intra-scale feature transform (Sec.3.1.1), our style loss (eq.5) is calculated as the summation of mean loss Huang & Belongie (2017) and covariance loss, i.e., square root of Frobenius distance between covariance matrices of feature maps of output and target style images. In case of using inter-scale feature transform (Sec.3.1.1), the summation of mean and covariance losses of the concatenated features are used as the style loss (eq.6).

EQUATION

EQUATION

where subscript o represents of output stylized image.

4. EXPERIMENTAL RESULT:

4.1 EXPERIMENTAL SETUP We used VGG16 feature extractor Simonyan & Zisserman (2014) as the encoder and a mirror-structured network as the decoder of our style transfer network. Our decoder network has 2 times larger number of channels in the corresponding layer of skipconnections than the previous methods Dumoulin et al. (2017); Huang & Belongie (2017) . {relu_1_2, relu_2_2, relu_3_3, relu_4_3} layers were used in calculating style loss and {relu_3_3} layer in calculating content loss. Here, we used the same content loss of Gatys et al. (2016) and our multi-scaled style loss in Sec.3.2. For training data set, MS-COCO train2014 Lin et al. (2014) and Painter By Numbers Kingma & Ba (2015) were used as content image set and large style image set respectively. Each dataset consists of about 80,000 images. And we used an additional small style image set of 77 style images to verify the effect of our proposed method as the number of training style increases. Each image was resized into 256 pixels in short side maintaining the original aspect ratio in both training and test phases, and, only for training phase, randomly cropped into (240, 240) pixels to avoid boundary artifact. We trained networks with 4 batches of random (content, style) image pairs, 4 epochs, and learning rate of 10 -4 . And all experiments were performed on Pytorch 0.3.1 framework with NVIDIA GTX 1080 Ti GPU card, CUDA 9.0, and CuDNN 7.0.

4.2. COMPARISON OF INTER-SCALE AND INTRA-SCALE FEATURE TRANSFORMS:

In order to verify the effect of our multi-scale feature transform for varying number of training style images, we trained two networks, one with the small style image set of 77 images and the other with the large style image set of about 80,000 images. Then we compared the output stylized images of the networks. Fig. 4 shows an example of the output stylized images using our intra-scale or inter-scale feature transform method. With the network trained by a small style image set, the result images generated by our intra-scale transform (fig. 4 (c )) show very similar texture style to the target style images (fig. 4 (b) ). And those by our inter-scale transform (fig. 4 (d) ) show even better style of texture. With the network trained by a large style image set (fig. 4 (e, d )), the result images also show the same tendency that inter-scale is better in expressing the texture of target style. Because of the existing correlations between scales as shown in fig. 2 (b) , inter-scale feature transform which considers interscale correlations shows the better quality of style than intra-scale transform. 2017) and (d) is Li et al. (2017b) .

Compared to Li et al. (2017b) , the generated images of our method present the styles closer to the target styles because our methods trained a decoder network to minimize style loss between target style and output images while Li et al. (2017b) trained its decoder network to minimize reconstruction loss of content images. We also compared our method to Avatar-Net Sheng et al. ( 2018) that performs multi-scale feature transform with a single decoder network. For a fair comparison, as the structure of Avatar-Net Sheng et al. ( 2018), we used VGG19 network Simonyan & Zisserman (2014) up to {relu_4_1} layer as the encoder and its mirrored structure as the decoder, and we also used additional style loss (eq.5 or eq.6) on image-level which is corresponding to the image reconstruction loss of Avatar-Net. As shown in fig. 8 , Both our intra (e) and inter (f) methods generated the stylized images with both detailed shapes of content images (a) and multi-scaled strokes of target style images (b). In contrast, the generated images of Avatar-Net (c, d) show somewhat deemed content shapes and blurred or burnt color patterns without detailed strokes. And selecting appropriate patch size corresponding to the scale of style pattern was necessary in Avatar-Net (in second row of fig. 8 , {patch size=1} (c) did not show large square patterns but {patch size=3} (d) did.) while our method did not require any scale matching parameter due to multi-scaled skip-connections in the decoder network.

4.4.2. COMPARISON IN CONTENT AND STYLE LOSSES:

For a quantitative comparison, we compared the content and style losses of the generated images of the existing methods and ours. Table .1 shows the measured average (standard deviation) losses across 847 tests of style transfer. Online optimization method Gatys et al. (2016) achieved the smallest style loss with a low content loss. Among the feed-forward networks trained with a small style image set, Dumoulin et al. ( 2017) achieved the lowest style loss (red colored numbers). This is because it has a learnable transformer and this resulted in a most optimized transformer. However, it is not extendable to arbitrary style transfer. Among the arbitrary style transferring methods, our method achieved the lowest style loss (blue colored numbers) with inter-scale feature transformer, and the second lowest style loss (green colored numbers) with intra-scale feature transformer. Among the feed-forward networks trained with a large style image set, our method shows the lowest style loss with inter-scale feature transformer and the second lowest style loss with intra-scale feature transformer in the same manner of the result with a small style image set. For the content loss with large style image set, the best method in style loss (our-inter) shows the highest content loss and the second best method (our-intra) shows the lowest content loss. This interesting result can be interpreted that inter-scale correlation has not only style of an image but also content of the image. Tasks of transferring style and preserving content are a trade-off in feedforward network methods. The result of Dumoulin et al. (2017) with small style image set also shows the same content/style trade-off. Therefore, we can select either inter-scale or intra-scale feature transformer according to user preference or purpose of application. 

5. CONCLUSION:

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.

:



Figures and Tables:

Figure 1: Network structure diagram for applying multi-scale stylization. (a) The cascade network scheme transfers style of an image into that of a target style image by using single scale transformers (SST) scale by scale. (b) Our multi-scaled style transformer (MST) transfers multi-scaled styles in a feed-forward process by using internal SSTs and a skip-connected decoder.

Figure 2: Correlation between channels in the multi-scaled feature map of the input image (a) extracted from the pre-trained VGG16 Simonyan & Zisserman (2014). 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.

Figure3: Each diagram shows the process of merging multi-scale features and dividing them into the original size for inter-scale feature transform. (a) Merging multi-scale features is performed as upsampling each scale feature by using nearest neighborhood interpolation to the largest size followed by concatenating them along the channel axis. (b) After feature transform, the transformed feature is split into multi-scale features and downsampled into its original size. Each split feature is inserted into the decoder layer of the corresponding scale by using skip-connection

Once feature maps of content and target style images are encoded, AdaIN directly adjusts the mean and standard deviation of a content feature map into those of a target style feature map, and then the adjusted feature map is decoded into an output image of the target style.Li et al. Li et al. (2017b)  further improved the arbitrary stylization by using covariance, instead of standard deviation, which considers the correlation between feature channels. To transfer multi-scaled style, Li et al.Li et al. (2017b)  used cascade networks, one scale of style transfer per network. Very recent feed-forward network approach Sheng et al. (2018) used multiple transformers in each scaled layer of their decoder network, where each transformer transfers corresponding scale of style and this reduced redundancy of cascade networks. However, their method did not consider the correlation between scales. Recent generative adversarial networks (GANs)Isola et al. (2017);Zhu et al. (2017a;b)  were used to transform a style per network as an application of their image translation. And there are also many methods dealt with various aspects of image style transfer such as photorealismLuan et al. (2017), spatial color controlGatys et al. (2017), and multi-style interpolationHuang & Belongie (2017);  Dumoulin et al. (2017);Ghiasi et al. (2017). But, those methods did not deal with multiple scales of style.

.3 SINGLE DECODER NETWORK WITH SKIP-CONNECTIONSTo utilize the transformed multi-scale features in generating output stylized image, decoding architecture of previous decoder network should be modified because each decoder layer has two input feature maps as fig.1(b), one is a decoded feature map from the previous decoder layer, the other is a (intra-scale or inter-scale) transformed feature from the transformer. We adopt skip-connection, which has been applied to several applications of computer vision fieldRonneberger et al. (2015);Mao et al. (2016);Isola et al. (2017);Bagautdinov et al. (2018) but not to image style transfer yet, to merge the two feature maps in decoding process as shown in fig.3 (b). Skip-connected two scale features are optimally merged by learnable convolution layer and this improves the quality of the decoded image by considering multi-scale filter responses.

Figure 4: Comparison of intra-scale and inter-scale transform: (a) input content image, (b) target style, (c,d) result of each model learned small style set and (e,f) results of learning large style set.

Figure5: Output stylized images according to the number of skip-connections: The (content, style) losses of output images are (c) (0.910, 0.535), (d) (0.707, 0.512), and (e) (0.814, 0.497). The more skip-connections are used, the less style loss is acheived. And there is a trade-off between style and content losses.4.3 ANALYSIS OF SKIP-CONNECTED DECODER NETWORKTo verity the effect of skip-connections in our style transfer network, we trained three different networks. The first one has the conventional single layer encoder/transformer/decoder architectureHuang & Belongie (2017);Li et al. (2017b)  which a sinlge feature transformer on {relu_3_3} without skip-connection. The second one has muti-scale feature transformers on {relu_3_3,relu_2_2} and one skip-connection on {relu_2_2}. The last one has multiscale feature transformers on {relu_3_3,relu_2_2,relu_1_2} and two skip-connections on {relu_2_2,relu_1_2}. Fig.5 shows an example of the output stylized image from the three different networks. As the number of skip-connections increases from fig.5(c) to (e), the style loss decreases from 0.535 to 0.497 and, accordingly, the color tone of the stylized image is getting better matched to the target style (fig.5(b)) and small patterns appear. To clarify the contributions of the skip-connected feature from the transformer and the decoded feature from the previous scale of decoder layer, we observed the absolute values of loss gradients with respect to the convolution weights of skip-connected decoder layers during the network learning process. As shown in fig.6(a), the gradient values for the skip-connected feature (channel indices from 129 to 256) on {relu_2_2} layer of decoder network are much larger than those for the decoded feature (channel indices from 1 to 128) at the beginning of training. This means that the skip-connected feature which already has target style through transformer dominantly affected to the decoder learning at the start of training phase. This happens because the previous decoder layer {relu_3_3} has random initial weights and outputs noisy feature at the start of training phase.As iteration goes, the gradient values for both features became similar to each other. This means that both skip-connected feature and decoded feature were equally utilized to generate an image of multi-scaled style. Fig.1(b)shows that the gradient values for the skip-connected feature (channel indices from 65 to 128) are smaller than those for the decoded feature (channel indices from 1 to 64) at the latter decoder layer. This means that the decoded feature of the latter decoder layer already has accumulated multi-scaled styles by the previous skip-connection and this resulted in the less impact of the skip-connected feature. However, using the skip-connection with the stylized feature of smaller scale has a certain effect on the result image in color tone matching as shown in fig.5(d,e).4.4 COMPARING WITH THE EXISTING METHODS4.4.1 COMPARISON IN QUALITY OF OUTPUT STYLIZED IMAGEWe compared the image quality of our method with those of the existing methodsGatys et al. (2016);Huang & Belongie (2017);Li et al. (2017b). We took the output stylized images forGatys et al. (2016) after 700 iterations of Adam optimization with learning rate 10 -1 , forHuang & Belongie (2017) with the same setting of our method except transformer and loss, and forLi et al. (2017b)  with style strength α = 0.6 (as mentioned in their work) and 3 cascade networks of VGG16 structure.

Figure6: Amplitude of loss gradients with resprct to the convolution weights in the skip-connected decoder layers during the learning process: The gradients are drawn every 500 iterations. The former half of the channels are for decoded feature from the previous scale and the latter are for skip-connected feature from transformer. Based on the gradients of 1st skip-connected layer (a) and 2nd skip-connected layer (b), the skip-connected (transformed) feature highly seems to affect to the decoder in initial interations but both decoded and transformed features samely affect as iteration goes. And the latter decoder layer (b) is less affected by the skip-connected feature than the former layer (a).

Figure 7: Output stylized images of the existing methods and our method: The target style images are not used for network learning.

Fig.7 shows the generated images from the existing and our intra/inter-scale feature transform methods. Compared to the online optimization method Gatys et al. (2016) (fig.7(c)), the other methods based on feed-forward network generated images of somewhat degraded style quality (fig.7(d e)). However, thanks to our muti-scaled style transfer which considering inter-channel or inter-scale correlation, texture detail and color tone of the generated image of our method with inter-scale (fig.7(f)) or intra-scale feature transform (fig.7(g)) are more similar to the target style than those of single-scale style transfer without considering inter-channel correlation Huang & Belongie (2017) (fig.7(d)) are.

Figure 8: Comparison of single-network-multi-scale transform methods (Avatar-Net Sheng et al. (2018) and Ours): the numbers in bottom-right of images are content loss values.

4.4.3 COMPARISON IN PROCESSING SPEED AND MEMOCY EFFICIENTYOur method achieved 31% less encoder/decoder feed-forward time (4.4 ms in average of 1000 trials with images of 240 by 240 pixels) and 4% less number of parameters (3,655,296 parameters) than the existing cascade network schemeLi et al. (2017b) (6.4 ms, 3,769,856 parameters).

, Mean-StdHuang & Belongie (2017), and reconstruction errorLi et al. (2017b), we adopt Mean-Std lossHuang & Belongie (2017) with some modification because of its consistency with AdaIN transformer. Instead of using Mean-Std loss as it is, we use Mean-Covariance loss to additionally consider interchannel and inter-scale correlations, which is consistent with our feature transformers described in Sec.3.1.

Table content: <table/>

Average (standard deviation) losses of various image style transferring methods. Each methods (a) is Gatys et al. (2016), (b) is Dumoulin et al. (2017), (c) is Huang & Belongie (

Table content: <table><tr><td colspan="3">Under review as a conference paper at ICLR 2019</td></tr><tr><td>Content Loss Inter-scale Covariance and Mean Inter-scale Gram Intra-scale Covariance and Mean Intra-scale Gram Mean, Std</td><td>On-line Learning (a) 9.94 (6.23) 0.30 (0.10) 0.11 (0.07) 0.32 (0.13) 0.14 (0.13) 0.13 (0.08)</td><td>Trained with Small Style Image Set Trained with Large Style Image Set (b) (c) intra inter (c) (d) intra inter 14.80 (6.90) 9.17 (3.06) 8.72 (4.41) 13.11 (5.02) 8.69 (2.50) 9.82 (2.07) 8.62 (3.81) 12.13 (4.23) 0.42 (0.19) 0.65 (0.31) 0.55 (0.24) 0.45 (0.22) 0.73 (0.41) 0.84 (0.42) 0.622 (0.33) 0.54 (0.31) 0.26 (0.31) 0.78 (0.98) 0.55 (0.66) 0.39 (0.53) 0.97 (1.52) 1.13 (1.60) 0.72 (1.10) 0.58 (0.94) 0.42 (0.22) 0.72 (0.36) 0.56 (0.26) 0.49 (0.25) 0.81 (0.47) 0.86 (0.46) 0.63 (0.35) 0.57 (0.34) 0.34 (0.51) 1.32 (1.74) 0.81 (1.12) 0.64 (0.95) 1.70 (2.97) 1.80 (2.88) 1.06 (1.85) 0.88 (1.61) 0.24 (0.18) 0.65 (0.37) 0.45 (0.27) 0.34 (0.26) 0.75 (0.53) 0.91 (0.57) 0.52 (0.35) 0.42 (0.33)</td></tr></table>

Peer review (iclr2019):
 The submission proposes a new model for the task of fast style transfer with arbitrary input images. The model combines the existing approach of Li et al. 2017 [1] and the idea of inter-layer correlations into a new model architecture for fast style transfer. Notably the idea of inter-layer correlations is not new, it was first proposed by Novak and Nikulin [2] and recently picked up by Yeh and Tang[3] (both methods are not referenced in the present submission). To evaluate the model, visual comparisons with other methods are shown and quantitative loss values for content and style loss are computed. My main issue with the submission is that the scope of the evaluation is not suited to demonstrate a clear advantage of the proposed method over existing work. The qualitative comparison remains highly subjective. Based on figure 7 I could not tell which method performs the best style transfer and this might well be because the systematic perceptual differences between the methods are small. To demonstrate a clear advance of the method in style transfer, I do believe it is necessary to run a user study showing that users systematically prefer the output of the proposed method over that of competing methods. Additionally the quantitative evaluation leaves several open questions: - Why is there no comparison with Ghiasi et al. 2017 [4]? This is the natural extension of Dumoulin et al. 2017 [5] for arbitrary fast style transfer. - What exactly is the content and style loss that these methods are compared on? Is it the one used for the models in this submission? If so, were the compared methods also trained to minimise this loss function? If not this does not seem to be a fair comparison. - Even if all methods were trained to minimise the same loss function, without user study it is unclear if that loss function is a good approximation of perceptual preference, which is the actual underlying target that style transfer aims to optimise. As a side note, the provided baseline of the Gatys et al. [6] method is somewhat misleading because it is using the Adam optimiser. The original work used LBFGS to optimise the loss function and it is fairly well known that the choice of optimiser can have significant impact on the quality of the results. I do not think this comparison is critical for the submission because the proposed method mainly competes with other fast style transfer methods, but I would recommend more care to be taken when reproducing existing work. All in all I think the submission proposes an interesting combination of existing methods leading to reasonable extension of the body of work in fast style transfer. However, given the lack of meaningful evaluation I am not convinced that the proposed method substantially advances the field of fast style transfer to warrant publication at ICLR. [1] Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, and Ming-Hsuan Yang. Universal style transfer via feature transforms. In Advances in Neural Information Processing Systems, pp. 385– 395, 2017b. [2] Improving the Neural Algorithm of Artistic Style. R Novak, Y Nikulin - arXiv preprint arXiv:1605.04603, 2016 - arxiv.org [3] Improved Style Transfer by Respecting Inter-layer Correlations MC Yeh, S Tang - arXiv preprint arXiv:1801.01933, 2018 - arxiv.org [4] Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, and Jonathon Shlens. Explor- ing the structure of a real-time, arbitrary neural artistic stylization network. In British Machine Vision Conference (BMVC), Sep 2017. [5] Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur. A learned representation for artistic style. In International Conference on Learning Representations (ICLR), Apr 2017. [6] Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. Image style transfer using convolutional neural networks. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.