Original: C ROSS -D OMAIN C ASCADED D EEP T RANSLATION
Revised: C ROSS -D OMAIN C ASCADED D EEP T RANSLATION

Original: Anonymous authors
Revised: Anonymous authors

Original: Paper under double-blind review
Revised: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: In recent years we have witnessed tremendous progress in unpaired image-toimage translation methods, propelled by the emergence of DNNs and adversarial training strategies.
Revised: In recent years we have witnessed tremendous progress in unpaired image-toimage translation methods, propelled by the emergence of DNNs and adversarial training strategies.

Original: However, most existing methods focus on transfer of style and appearance , rather than on shape translation.
Revised: However, most existing methods focus on transfer of style and appearance , rather than on shape translation.

Original: The latter task is challenging, due to its intricate non-local nature, which calls for additional supervision.
Revised: The latter task is challenging, due to its intricate non-local nature, which calls for additional supervision.

Original: We mitigate this by descending the deep layers of a pre-trained network, where the deep features contain more semantics, and applying the translation between these deep features.
Revised: We mitigate this by descending the deep layers of a pre-trained network, where the deep features contain more semantics, and applying the translation between these deep features.

Original: Speciﬁcally, we leverage VGG, which is a classiﬁcation network, pre-trained  with large-scale semantic supervision.
Revised: Speciﬁcally, we leverage VGG, which is a classiﬁcation network, pre-trained on ImageNet with large-scale semantic supervision.

Original: Our translation is performed in a cascaded, deep-to-shallow, fashion, along the deep feature hierarchy: we ﬁrst translate between the deepest layers that encode the higher-level semantic content of the image, proceeding to translate the shallower layers, conditioned on the deeper ones.
Revised: Our translation is performed in a cascaded, deep-to-shallow, fashion, along the deep feature hierarchy: we ﬁrst translate between the deepest layers that encode the higher-level semantic content of the image, proceeding to translate the shallower layers, conditioned on the deeper ones.

Original: We show that our method is able to translate between different domains, which exhibit signiﬁcantly different shapes.
Revised: We show that our method is able to translate between different domains, which exhibit signiﬁcantly different shapes.

Original: We evaluate our method both qualitatively and quantitatively and compare it to state-of-the-art image-to-image translation methods.
Revised: We evaluate our method both qualitatively and quantitatively and compare it to state-of-the-art image-to-image translation methods.

Original: Our code and trained models will be made available.
Revised: Our code and trained models will be made available.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: In recent years, neural networks have signiﬁcantly advanced generative image modeling.
Revised: In recent years, neural networks have signiﬁcantly advanced generative image modeling.

Original: Following the emergence of Generative Adversarial Networks (GANs) (Goodfellow et al., 2014), image-to-image translation methods have dramatically progressed, revolutionizing applications such as inpainting (Yu et al., 2018), super resolution (Wang et al., 2018), domain adaptation (Hoffman et al., 2017), and more.
Revised: Following the emergence of Generative Adversarial Networks (GANs) (Goodfellow et al., 2014), image-to-image translation methods have dramatically progressed, revolutionizing applications such as inpainting (Yu et al., 2018), super resolution (Wang et al., 2018), domain adaptation (Hoffman et al., 2017), and more.

Original: In particular, there have been intriguing advances in the setting of unpaired image-to-image translation through the use of cycle-consistency (Yi et al., 2017; Zhu et al., 2017) as well as other approaches (Benaim & Wolf, 2017; Huang et al., 2018; Lee et al., 2018; Ma et al., 2018).
Revised: In particular, there have been intriguing advances in the setting of unpaired image-to-image translation through the use of cycle-consistency (Yi et al., 2017; Zhu et al., 2017) as well as other approaches (Benaim & Wolf, 2017; Huang et al., 2018; Lee et al., 2018; Ma et al., 2018).

Original: However, most existing methods acknowledge the difﬁculty in translating shapes from one domain to another, as this might entail highly non-trivial geometric deformations.
Revised: However, most existing methods acknowledge the difﬁculty in translating shapes from one domain to another, as this might entail highly non-trivial geometric deformations.

Original: Consider, for example, translating between elephants and giraffes, where we would expect the neck of an elephant to be extended, while the elephant’s head should shrink.
Revised: Consider, for example, translating between elephants and giraffes, where we would expect the neck of an elephant to be extended, while the elephant’s head should shrink.

Original: The challenge is compounded by the fact that, even within the same domain, images might exhibit extreme variations in object shape and pose, partial occlusions, and contain multiple instances of the object of interest.
Revised: The challenge is compounded by the fact that, even within the same domain, images might exhibit extreme variations in object shape and pose, partial occlusions, and contain multiple instances of the object of interest.

Original: One might even argue that this translation task is ill-posed to begin with, and at the very least, requires high-level semantics to be accounted for.
Revised: One might even argue that this translation task is ill-posed to begin with, and at the very least, requires high-level semantics to be accounted for.

Original: Nonetheless, several works do address shape deformation in the context of image-to-image translation by requiring supervision in the form of a foreground mask (Liang et al., 2018; Mo et al., 2018).
Revised: Nonetheless, several works do address shape deformation in the context of image-to-image translation by requiring supervision in the form of a foreground mask (Liang et al., 2018; Mo et al., 2018).

Original: In contrast, GANimorph (Gokaslan et al., 2018) and the recently proposed TransGaGa (Wu et al., 2019) show remarkable translation without requiring additional supervision for several datasets.
Revised: In contrast, GANimorph (Gokaslan et al., 2018) and the recently proposed TransGaGa (Wu et al., 2019) show remarkable translation without requiring additional supervision for several datasets.

Original: However, these techniques excel in controlled setting only, where the images are controlled, and the foreground separation is rather simple.
Revised: However, these techniques excel in controlled setting only, where the images are controlled, and the foreground separation is rather simple.

Original: In this paper, we address the problem of unpaired image-to-image translation, without requiring foreground masks, between two different domains, where the objects of interest share some semantic similarity (e.g., four-legged mammals), whose shapes and appearances may, nevertheless, be drastically different.
Revised: In this paper, we address the problem of unpaired image-to-image translation, without requiring foreground masks, between two different domains, where the objects of interest share some semantic similarity (e.g., four-legged mammals), whose shapes and appearances may, nevertheless, be drastically different.

Original: Our key idea is to accomplish the translation task by learning to translate between deep feature maps.
Revised: Our key idea is to accomplish the translation task by learning to translate between deep feature maps.

Original: Rather than learning to extract the relevant higher-level semantic information for the speciﬁc pair of domains at hand, we leverage deep features extracted by a network pre-trained for image classiﬁcation, thereby beneﬁting from its large-scale fully supervised training. 
Revised: Rather than learning to extract the relevant higher-level semantic information for the speciﬁc pair of domains at hand, we leverage deep features extracted by a network pre-trained for image classiﬁcation, thereby beneﬁting from its large-scale fully supervised training. may very similar from different

Original: Our work is motivated by the well-known observation that neurons in the deeper layers of pre-trained classiﬁcation networks represent larger receptive ﬁelds in image space, and encode higher-level semantic content Zeiler & Fergus (2014). In other words, local activation patterns in the deeper layers may encode very different shapes in size and structure.
Revised: .g

Original: Furthermore, Aberman et al.
Revised: and

Original: (2018) showed that semantically similar regions from different domains, e.g. dog and cat, have similaractivations.
Revised: , have similar

Original: That is, the encoding of a cat’s eye resembles that of a dog’s eye more than that of its tail.
Revised: That is, the encoding of a cat’s eye resembles that of a dog’s eye more than that of its tail.

Original: From the point of view of the translation task, these properties are attractive, since they suggest that it might be possible to learn a semantically consistent translation between activation patterns produced by images from different domains, and that the resulting (reconstructed) image would be able to change drastically, hopefully bypassing the common difﬁculties in image-to-image translation methods.
Revised: From the point of view of the translation task, these properties are attractive, since they suggest that it might be possible to learn a semantically consistent translation between activation patterns produced by images from different domains, and that the resulting (reconstructed) image would be able to change drastically, hopefully bypassing the common difﬁculties in image-to-image translation methods.

Original: More speciﬁcally, we learn to translate between several layers of deep feature maps, extracted from two domains by a pre-trained classiﬁcation network, namely VGG-19 (Simonyan & Zisserman, 2014).
Revised: More speciﬁcally, we learn to translate between several layers of deep feature maps, extracted from two domains by a pre-trained classiﬁcation network, namely VGG-19 (Simonyan & Zisserman, 2014).

Original: The translation is carried out one layer at a time in a deep-to-shallow (coarse-to-ﬁne)
Revised: The translation is carried out one layer at a time in a deep-to-shallow (coarse-to-ﬁne)

Original: cascaded manner.
Revised: cascaded manner.

Original: For each layer, we adversarially train a dedicated translator that acts in the features space of that layer.
Revised: For each layer, we adversarially train a dedicated translator that acts in the features space of that layer.

Original: The deepest layer translator effectively learns to translate between semantically similar global structures, such as body shape or head position, as demonstrated by the middle pair of images in Figure 1.
Revised: The deepest layer translator effectively learns to translate between semantically similar global structures, such as body shape or head position, as demonstrated by the middle pair of images in Figure 1.

Original: The translator of each shallower layer is conditioned on the translation result of the previous layer, and learns to add ﬁne scale and appearance details, such as texture.
Revised: The translator of each shallower layer is conditioned on the translation result of the previous layer, and learns to add ﬁne scale and appearance details, such as texture.

Original: At every layer, in order to visualize the generated deep features, we use a network pre-trained for inverting the deep features of VGG-19, following the method of Dosovitskiy & Brox (2016).
Revised: At every layer, in order to visualize the generated deep features, we use a network pre-trained for inverting the deep features of VGG-19, following the method of Dosovitskiy & Brox (2016).

Original: The images shown in
Revised: The images shown in

Original: Figure 1 were generated in this manner.
Revised: Figure 1 were generated in this manner.

Revised: Our conceptual novelty, can be viewed as a transfer learning for image translation as we are translating high level semantics, encoded in the deeper layers of a pre-trained classiﬁcation network, a.k.a deep features.

Revised: This is in contrast to existing methods Gokaslan et al.

Revised: ; Wu et al.

Revised: (2019), which learn to translate the images directly.

Original: We compare our method with several state-of-the-art image translation methods.
Revised: We compare our method with several state-of-the-art image translation methods.

Original: To demonstrate the effectiveness of our approach, we present results for several challenging pairs of domains that exhibit drastically different shapes and appearances, but share some high-level semantics.
Revised: To demonstrate the effectiveness of our approach, we present results for several challenging pairs of domains that exhibit drastically different shapes and appearances, but share some high-level semantics.

Original: Our translations are semantically consistent, typically preserving pose and number of instances of objects of interest, and reproducing their partial occlusion or cropping, as may be seen in Figure 5.
Revised: Our translations are semantically consistent, typically preserving pose and number of instances of objects of interest, and reproducing their partial occlusion or cropping, as may be seen in Figure 5.

Original:  R ELATED WORK
Revised:  R ELATED WORK

Original: Zhu et al.
Revised: Zhu et al.

Original: (2017) (simultaneously with Yi et al.
Revised: (2017) (simultaneously with Yi et al.

Original: (2017) and Kim et al.
Revised: (2017) and Kim et al.

Original: (2017)) have presented remarkable unpaired image-to-image translations, using a framework known as CycleGAN.
Revised: (2017)) have presented remarkable unpaired image-to-image translations, using a framework known as CycleGAN.

Original: The key idea is that the ill-posed conditional generative process can be regularized by a cycle-consistency constraint, which enforces the translation to perform a bijective mapping.
Revised: The key idea is that the ill-posed conditional generative process can be regularized by a cycle-consistency constraint, which enforces the translation to perform a bijective mapping.

Original: The cycle constraint has become a popular regularization technique for unpaired image-to-image translation.
Revised: The cycle constraint has become a popular regularization technique for unpaired image-to-image translation.

Original: For example, the
Revised: For example, the

Original: UNIT framework (Liu et al., 2017) assumes a shared latent space between the domains and enforces the cycle constraint in the shared latent space.
Revised: UNIT framework (Liu et al., 2017) assumes a shared latent space between the domains and enforces the cycle constraint in the shared latent space.

Original: Several works were developed to extend the one-to-one mapping to many-to-many mapping (Ma et al., 2018; Huang et al.,2018; Lee et al., 2018; Almahairi et al., 2018).
Revised: Several works were developed to extend the one-to-one mapping to many-to-many mapping (Ma et al., 2018; Huang et al., 2018).

Original: These methods decompose the encoding space to shared latent space, representing the domain invariant content space, and domain speciﬁc style space.
Revised: These methods decompose the encoding space to shared latent space, representing the domain invariant content space, and domain speciﬁc style space.

Original: Therefore, many translations can be achieved from a single content code by changing the style code of the input image.
Revised: Therefore, many translations can be achieved from a single content code by changing the style code of the input image.

Original: Many translation methods share the inability to translate high-level semantics, including different shape geometry.
Revised: Many translation methods share the inability to translate high-level semantics, including different shape geometry.

Original: This type of translation is usually necessary in the case of transﬁguration, where one aims to transform a speciﬁc type of objects without changing the background.
Revised: This type of translation is usually necessary in the case of transﬁguration, where one aims to transform a speciﬁc type of objects without changing the background.

Original: Both Lee et al.
Revised: Both Lee et al.

Original: and Mejjati et al.
Revised: and Mejjati et al.

Original: (2018) learn an attention map and apply translation only on the the foreground object.
Revised: (2018) learn an attention map and apply translation only on the the foreground object.

Original: However, both methods only improve translations that do not deform shapes.
Revised: However, both methods only improve translations that do not deform shapes.

Original: Gokaslan et al.
Revised: Gokaslan et al.

Original: (2018) succeed in preforming several shape-deforming translations by several modiﬁcations to the
Revised: (2018) succeed in preforming several shape-deforming translations by several modiﬁcations to the

Original: CycleGAN framework, including using dilated convolutions in the discriminator.
Revised: CycleGAN framework, including using dilated convolutions in the discriminator.

Original: However, they have not demonstrated strong shape deformations, such as zebras to elephants or giraffes, as we show in
Revised: However, they have not demonstrated strong shape deformations, such as zebras to elephants or giraffes, as we show in

Original: Section 4.
Revised: Section 4.

Original: Liang et al.
Revised: Liang et al.

Original: (2018) and Mo et al.
Revised: (2018) and Mo et al.

Original: (2018) assume some kind of segmentation is given, and use this segmentation to guide shape deformation translation.
Revised: (2018) assume some kind of segmentation is given, and use this segmentation to guide shape deformation translation.

Original: However, such segmentation is hard to achieve.
Revised: However, such segmentation is hard to achieve.

Original: In a recent work, Wu et al.
Revised: In a recent work, Wu et al.

Original: (2019) disentangle the input images to geometry and appearance spaces, relying on high intra-consistency, and learn to translate each of the two domains separately.
Revised: (2019) disentangle the input images to geometry and appearance spaces, relying on high intra-consistency, and learn to translate each of the two domains separately.

Original: However, the variation of geometry and appearance of in-the-wild images is too large to be disentangled successfully 1 .
Revised: However, the variation of geometry and appearance of in-the-wild images is too large to be disentangled successfully 1 .

Original: Contrary to the above works, our work leverages a pre-trained network and the translation is applied directly on deep feature maps, thus being guided by high-level semantics.
Revised: Contrary to the above works, our work leverages a pre-trained network and the translation is applied directly on deep feature maps, thus being guided by high-level semantics.

Original: Several image-to-image methods, such as Xu et al.
Revised: Several image-to-image methods, such as Xu et al.

Original: ; Di et al.
Revised: ; Di et al.

Original: ; Ignatov et al.
Revised: ; Ignatov et al.

Original: (2018), also incorporate such pretrained networks, though usually, only as perceptual loss, constraining the translated image to remain semantically close to the input image.
Revised: (2018), also incorporate such pretrained networks, though usually, only as perceptual loss, constraining the translated image to remain semantically close to the input image.

Original: Differently, Sungatullina et al.
Revised: Differently, Sungatullina et al.

Original: (2018) incorporate pre-trained
Revised: (2018) incorporate pre-trained

Original: VGG features into the discriminator architecture, to assist in the discrimination phase.
Revised: VGG features into the discriminator architecture, to assist in the discrimination phase.

Original: Wu et al.
Revised: Wu et al.

Original: (2018) use VGG-19 as a ﬁxed encoder in the translation, where only the decoder is learned.
Revised: (2018) use VGG-19 as a ﬁxed encoder in the translation, where only the decoder is learned.

Original: Upchurch et al.
Revised: Upchurch et al.

Original: (2017) present the only method, to our knowledge, that actually translates deep features between two domains.
Revised: (2017) present the only method, to our knowledge, that actually translates deep features between two domains.

Original: However, the translation is not learned, but deﬁned by simply interpolating between the deep features, which restricts the scope of method to highly aligned domains.
Revised: However, the translation is not learned, but deﬁned by simply interpolating between the deep features, which restricts the scope of method to highly aligned domains.

Original: For completeness, we also mention that Yin et al.
Revised: For completeness, we also mention that Yin et al.

Original: (2019) train an autoencoder to embed point clouds, and perform translation in the learned embedding.
Revised: (2019) train an autoencoder to embed point clouds, and perform translation in the learned embedding.

Original: In contrast, we utilize semantics to preform the translation in the much more difﬁcult scenario of images.
Revised: In contrast, we utilize semantics to preform the translation in the much more difﬁcult scenario of images.

Original: Our work shares some similarities with the early work of Huang et al.
Revised: Our work shares some similarities with the early work of Huang et al.

Original: (2017), which suggests using a generative adversarial model (Goodfellow et al., 2014) in a coarse-to-ﬁne manner with respect to a pre-trained encoder.
Revised: (2017), which suggests using a generative adversarial model (Goodfellow et al., 2014) in a coarse-to-ﬁne manner with respect to a pre-trained encoder.

Original: The generation process begins from the deepest features and then recursively synthesizes shallower layers conditioned on the deeper layer, until generating the ﬁnal image.
Revised: The generation process begins from the deepest features and then recursively synthesizes shallower layers conditioned on the deeper layer, until generating the ﬁnal image.

Original: This method was only applied on small encoders and low resolution images and was not explored for very deep and semantic encoding neural networks such as VGG-19 (Simonyan & Zisserman, 2014).
Revised: This method was only applied on small encoders and low resolution images and was not explored for very deep and semantic encoding neural networks such as VGG-19 (Simonyan & Zisserman, 2014).

Original: Deep image analogies (Liao et al., 2017) transfer visual attributes between semantically similar images, by feed-forwarding them through a pre-trained network.
Revised: Deep image analogies (Liao et al., 2017) transfer visual attributes between semantically similar images, by feed-forwarding them through a pre-trained network.

Original: Their work does not train a generative model; nonetheless, they create new deep features by fusing content features from one image with style features of another.
Revised: Their work does not train a generative model; nonetheless, they create new deep features by fusing content features from one image with style features of another.

Original: Similarly, Aberman et al.
Revised: Similarly, Aberman et al.

Original: (2018) synthesize hybrid images from two aligned images by selecting the dominant deep features response.
Revised: (2018) synthesize hybrid images from two aligned images by selecting the dominant deep features response.

Original:  M ETHOD
Revised:  M ETHOD

Original: Our general setting is similar to that of previous unpaired image-to-image translation methods.
Revised: Our general setting is similar to that of previous unpaired image-to-image translation methods.

Original: Given images from two domains, A and B , our goal is to learn to translate between them.
Revised: Given images from two domains, A and B , our goal is to learn to translate between them.

Original: However, unlike other image-to-image translation methods, we perform the translation on the deep features extracted by a pre-trained classiﬁcation network, speciﬁcally VGG-19 (Simonyan & Zisserman, 2014).
Revised: However, unlike other image-to-image translation methods, we perform the translation on the deep features extracted by a pre-trained classiﬁcation network, speciﬁcally VGG-19 (Simonyan & Zisserman, 2014).

Original: The translation is carried out in a deep-to-shallow (coarse-to-ﬁne) manner, using a cascade of pairs of translators, one pair per layer.
Revised: The translation is carried out in a deep-to-shallow (coarse-to-ﬁne) manner, using a cascade of pairs of translators, one pair per layer.

Original: The entire architecture used to train the translators is shown schematically in Figure 2, while Figure 3 illustrates the test-time translation (inference) process.
Revised: The entire architecture used to train the translators is shown schematically in Figure 2, while Figure 3 illustrates the test-time translation (inference) process.

Original: Oncethe deepest feature map has been translated, we translate the next (shallower and less semantic feature map), conditioned on the translated deeper layer. In this manner, the translation of the shallower map preserves the general structure of the translated deeper one, but adds ﬁner details, which are not encoded in the deeper feature maps.
Revised: Once

Original: We repeat this procedure until the original image level is reached.
Revised: [Figure]

Original: Below we describe the training and the inference processes in more detail.
Revised: [Figure]

Original: Pre-processing: We extract high-level semantic features from input images from both domains,
Revised: Pre-processing: We extract high-level semantic features from input images from both domains,

Original: A and B , by feed-forwarding the images through the pre-trained VGG-19 (Simonyan & Zisserman,[Figure] [Figure] 2014) network.
Revised: A and B , by feed-forwarding the images through the pre-trained VGG-19 (Simonyan & Zisserman, 2014) network.

Original: Next, we sample ﬁve of the resulting deep feature maps, speciﬁcally conv_i_ ( i = 1 , 2 , 3 , 4 , 5 ), where each map has progressively coarser spatial resolution, but a larger number channels.
Revised: Next, we sample ﬁve of the resulting deep feature maps, speciﬁcally conv_i_ ( i = 1 , 2 , 3 , 4 , 5 ), where each map has progressively coarser spatial resolution, but a larger number channels.

Original: We denote the i -th sampled feature map for image a ∈ A as a i .
Revised: We denote the i -th sampled feature map for image a ∈ A as a i .

Original: Since propagation through the pre-trained VGG-19 network may yield features in any range, while the range of the synthesized features is usually known, to ease the translation, we ﬁrst normalize each channel, of every layer i , by calculating its mean and standard deviation across the domain and then we clamp the normalized feature values to the range of [ − 1 , 1] .
Revised: Since propagation through the pre-trained VGG-19 network may yield features in any range, while the range of the synthesized features is usually known, to ease the translation, we ﬁrst normalize each channel, of every layer i , by calculating its mean and standard deviation across the domain and then we clamp the normalized feature values to the range of [ − 1 , 1] .

Original: While the clamping is a potentially harmful irreversible operation, we did not observe any adverse effect on the results.
Revised: While the clamping is a potentially harmful irreversible operation, we did not observe any adverse effect on the results.

Original: We use A i ( B i ) to denote set of all normalized deep feature maps of level i , extracted from images in domain A ( B ).
Revised: We use A i ( B i ) to denote set of all normalized deep feature maps of level i , extracted from images in domain A ( B ).

Original: Inference: We perform the translation in a coarse-to-ﬁne fashion.
Revised: Inference: We perform the translation in a coarse-to-ﬁne fashion.

Original: Thus, the translator from domain
Revised: Thus, the translator from domain

Original: A to B , actually consists of a sequence of translators (cid:8) G 5 B , G 4 B , . . .
Revised: A to B , actually consists of a sequence of translators (cid:8) G 5 B , G 4 B , . . .

Original: , G 1 B (cid:9) , where each translator is responsible for translating the i -th feature map layer a i , from A i to B i conditioned on the previously translated deeper layer ˜
Revised: , G 1 B (cid:9) , where each translator is responsible for translating the i -th feature map layer a i , from A i to B i conditioned on the previously translated deeper layer ˜

Original: b i +1 (except for the deepest layer translator G 5 B , which is unconditioned).
Revised: b i +1 (except for the deepest layer translator G 5 B , which is unconditioned).

Original: Finally, G 0 B uses feature inversion to convert ˜ b 1 to obtain the translated image.
Revised: Finally, G 0 B uses feature inversion to convert ˜ b 1 to obtain the translated image.

Original: The translators G iA from domain B i to A i are deﬁned symmetrically.
Revised: The translators G iA from domain B i to A i are deﬁned symmetrically.

Original: The entire inference pipeline is shown in Figure 3.
Revised: The entire inference pipeline is shown in Figure 3.

Original: Feature inversion:
Revised: Feature inversion:

Original: In all the results we show, e.g., Figure 1, we visualize the output of the various translators by pre-training a deep feature inversion network (per domain), for each layer i = 1 , . . .
Revised: In all the results we show, e.g., Figure 1, we visualize the output of the various translators by pre-training a deep feature inversion network (per domain), for each layer i = 1 , . . .

Original: , 5 , following Dosovitskiy & Brox (2016).
Revised: , 5 , following Dosovitskiy & Brox (2016).

Original: The network aims to reconstruct the original image given the feature map of a speciﬁc layer, regularized by adversarial loss so that the reconstructed image would lie in the manifold of natural images.
Revised: The network aims to reconstruct the original image given the feature map of a speciﬁc layer, regularized by adversarial loss so that the reconstructed image would lie in the manifold of natural images.

Original: For more details we refer the reader to Dosovitskiy & Brox (2016).
Revised: For more details we refer the reader to Dosovitskiy & Brox (2016).

Original: The speciﬁc settings used in our implementation are elaborated in Appendix A.1.
Revised: The speciﬁc settings used in our implementation are elaborated in Appendix A.1.

Original: Deepest layer translation: We begin by translating the deepest feature maps, encoding the highestlevel semantic features, i.e., A 5 and B 5 , hence, our problem is reduced to translating high-dimensional tensors.
Revised: Deepest layer translation: We begin by translating the deepest feature maps, encoding the highestlevel semantic features, i.e., A 5 and B 5 , hence, our problem is reduced to translating high-dimensional tensors.

Original: Our solution builds upon the commonly used CycleGAN framework (Zhu et al., 2017).
Revised: Our solution builds upon the commonly used CycleGAN framework (Zhu et al., 2017).

Original: Speciﬁcally, we use the three losses proposed in Zhu et al.
Revised: Speciﬁcally, we use the three losses proposed in Zhu et al.

Original: First, in order to generate deep features in the appropriate domain, we utilize an adversarial domain loss L adv .
Revised: First, in order to generate deep features in the appropriate domain, we utilize an adversarial domain loss L adv .

Original: We simultaneously train two translators G 5 A , G 5 B which try to fool domain-speciﬁc discriminators, D 5 A , D 5 B (for domains
Revised: We simultaneously train two translators G 5 A , G 5 B which try to fool domain-speciﬁc discriminators, D 5 A , D 5 B (for domains

Original: A 5 , B 5 , respectively).
Revised: A 5 , B 5 , respectively).

Original: However, differently from Zhu et al.
Revised: However, differently from Zhu et al.

Original: (2017) and other image translation methods (Huang et al., 2018; Mo et al., 2018), we have found LSGAN (Mao et al., 2017) not to be well-suited for our task, leading to mode collapse or convergence failures.
Revised: (2017) and other image translation methods (Huang et al., 2018; Mo et al., 2018), we have found LSGAN (Mao et al., 2017) not to be well-suited for our task, leading to mode collapse or convergence failures.

Original: Instead, we found
Revised: Instead, we found

Original: WGAN-GP (Gulrajani et al., 2017) more effective, thus, the adversarial loss for translation from X to Y is deﬁned as
Revised: WGAN-GP (Gulrajani et al., 2017) more effective, thus, the adversarial loss for translation from X to Y is deﬁned as

Original: [Equation]
Revised: [Equation]

Original: where G Y : X → Y is the translator, D Y is the target domain discriminator, λ gp = 10 in all our experiments, and P ˆ Y is deﬁned by uniformly sampling along straight lines between ˜ y ∼ G ( P X ) and y ∼ P Y .
Revised: where G Y : X → Y is the translator, D Y is the target domain discriminator, λ gp = 10 in all our experiments, and P ˆ Y is deﬁned by uniformly sampling along straight lines between ˜ y ∼ G ( P X ) and y ∼ P Y .

Original: For more details we refer the reader to Gulrajani et al.
Revised: For more details we refer the reader to Gulrajani et al.

Original: Second, for regularizing the translation to one-to-one mapping, we add the cycle constraint,
Revised: Second, for regularizing the translation to one-to-one mapping, we add the cycle constraint,

Original: [Equation]
Revised: [Equation]

Original: where (cid:107) · (cid:107) stands for the L 1 norm.
Revised: where (cid:107) · (cid:107) stands for the L 1 norm.

Original: Finally, as in Zhu et al.
Revised: Finally, as in Zhu et al.

Original: (2017), we have found it helpful to use an identity loss, which guides the networks to preserve common high level features,
Revised: (2017), we have found it helpful to use an identity loss, which guides the networks to preserve common high level features,

Original: [Equation]
Revised: [Equation]

Original: The entire loss combines these components as follows
Revised: The entire loss combines these components as follows

Original: [Equation]
Revised: [Equation]

Original: where λ cyc and λ idty were set to 100 in all our experiments.
Revised: where λ cyc and λ idty were set to 100 in all our experiments.

Original: Coarse to ﬁne conditional translation: Consider two successive layers, a
Revised: Coarse to ﬁne conditional translation: Consider two successive layers, a

Original: i ∈ A i and a
Revised: i ∈ A i and a

Original: i +1 ∈
Revised: i +1 ∈

Original: A i +1 , where the latter has already been translated, yielding
Revised: A i +1 , where the latter has already been translated, yielding

Original: ˜ b
Revised: ˜ b

Original: i +1 as the translation outcome (see Figure 3).
Revised: i +1 as the translation outcome (see Figure 3).

Original: We next perform the translation of the layer a i to yield
Revised: We next perform the translation of the layer a i to yield

Original: ˜ b
Revised: ˜ b

Original: i , using the translator G iB , conditioned on ˜ b
Revised: i , using the translator G iB , conditioned on ˜ b

Original: i +1 .
Revised: i +1 .

Original: Note that G iB is effectively a function of all the previously translated layers.
Revised: Note that G iB is effectively a function of all the previously translated layers.

Original: The architecture of G iB is schematically shown in Figure 4.
Revised: The architecture of G iB is schematically shown in Figure 4.

Original: Since shallower layers encode less of the semantic content of the image, it is more difﬁcult to learn how they should be deformed, and thus they are used to transfer “style”, while the “content” comes from the already translated deeper layer.
Revised: Since shallower layers encode less of the semantic content of the image, it is more difﬁcult to learn how they should be deformed, and thus they are used to transfer “style”, while the “content” comes from the already translated deeper layer.

Original: Inspired by Huang et al.
Revised: Inspired by Huang et al.

Original: (2018), we add an adaptive instance normalization (AdaIN) (Huang &
Revised: (2018), we add an adaptive instance normalization (AdaIN) (Huang &

Original: Belongie, 2017) component, whose parameters are learned from the current layer.
Revised: Belongie, 2017) component, whose parameters are learned from the current layer.

Original: Thus, several layers of G iB are normalized according to the AdaIN component.
Revised: Thus, several layers of G iB are normalized according to the AdaIN component.

Original: G iA , which is designed symmetrically, is learned simultaneously with G iB , as shown in Fig.4(a).
Revised: G iA , which is designed symmetrically, is learned simultaneously with G iB , as shown in Fig.4(a).

Original: The loss for training these shallower translators is roughly the same as that used for training the deepest translation: it consists of adversarial domain loss, cycle constraint loss, and identity loss.
Revised: The loss for training these shallower translators is roughly the same as that used for training the deepest translation: it consists of adversarial domain loss, cycle constraint loss, and identity loss.

Original:  While we formalize the adversarial loss unconditionally, similarly to equation 1, the cyclic loss is now conditioned: (cid:13)(cid:13)(cid:13) G iA (cid:16) G iB (cid:16) a i , ˜ b i +1 (cid:17) , a i +1 (cid:17) − a i (cid:13)(cid:13)(cid:13) + (cid:13)(cid:13) G iB (cid:0) G iA ( b i , ˜ a i +1 ) , b i +1 (cid:1)
Revised: [Figure] While we formalize the adversarial loss unconditionally, similarly to equation 1, the cyclic loss is now conditioned: (cid:13)(cid:13)(cid:13) G iA (cid:16) G iB (cid:16) a i , ˜ b i +1 (cid:17) , a i +1 (cid:17) − a i (cid:13)(cid:13)(cid:13) + (cid:13)(cid:13) G iB (cid:0) G iA ( b i , ˜ a i +1 ) , b i +1 (cid:1)

Original: − b i (cid:13)(cid:13) , and the same is true for the identity loss (cid:13)(cid:13)
Revised: − b i (cid:13)(cid:13) , and the same is true for the identity loss (cid:13)(cid:13)

Original: G iA ( a i , a i +1 ) − a i (cid:13)(cid:13)
Revised: G iA ( a i , a i +1 ) − a i (cid:13)(cid:13)

Original: + (cid:13)(cid:13)
Revised: + (cid:13)(cid:13)

Original: G iB ( b i , b i +1 )
Revised: G iB ( b i , b i +1 )

Original: − b i (cid:13)(cid:13) .
Revised: − b i (cid:13)(cid:13) .

Original: We train the pairs of translators one layer at a time, starting from G 5 A and G 5 B .
Revised: We train the pairs of translators one layer at a time, starting from G 5 A and G 5 B .

Original: More details regarding the implementation and the training of the translators are included in Appendix A.1.
Revised: More details regarding the implementation and the training of the translators are included in Appendix A.1.

Original:  E XPERIMENTS
Revised:  E XPERIMENTS

Original: We evaluate our methods on several publicly available datasets: (1) Cat ↔ dog faces (Lee et al., 2018), which contains 871 cats images and 1364 dogs images and does not require high shape deformation;[Figure] (2) Kaggle Cat ↔ dog (Elson et al., 2007) dataset with over 12 , 500 images in each domain (the images may contain part of the object or several instances); (3) Challenging MSCOCO dataset (Lin et al., 2014), speciﬁcally, zebra ↔ elephant and zebra ↔ giraffe (the number of images is each category is reported in Lin et al.
Revised: We evaluate our methods on several publicly available datasets: (1) Cat ↔ dog faces (Lee et al., 2018), which contains 871 cats images and 1364 dogs images and does not require high shape deformation; (2) Kaggle Cat ↔ dog (Elson et al., 2007) dataset with over 12 , 500 images in each domain (the images may contain part of the object or several instances); (3) Challenging MSCOCO dataset (Lin et al., 2014), speciﬁcally, zebra ↔ elephant and zebra ↔ giraffe (the number of images is each category is reported in (overall there are 1917 zebras, 2547 giraffes and 2144 elephants).

Original: We note that no previous method has used MSCOCO, without supervision in the form of segmentation. 
Revised: We note that no previous method has used MSCOCO, without supervision in the form of segmentation. [Figure] [Figure]

Revised: → / ←

Revised: [Figure]

Original:  Our deepest translators, i.e., G 5 A , G 5 B , consist of encoder-decoder structure with several strided convolutional layers followed by symmetric transpose convolutional layers.
Revised: Cat ↔ Dog 126.93/127. Zebra ↔ Giraffe 167.62/184. Zebra ↔ Elephant 101.26/ 105.58/57. Our deepest translators, i.e., G 5 A , G 5 B , consist of encoder-decoder structure with several strided convolutional layers followed by symmetric transpose convolutional layers.

Original: We use group normalization (Wu & He, 2018) and ReLU activation function (except the last layer, which is tanh ).
Revised: We use group normalization (Wu & He, 2018) and ReLU activation function (except the last layer, which is tanh ).

Original: The conditional generators, consist of learned AdaIN layer, achieved by several strided convolutional layers followed by fully connected layers.
Revised: The conditional generators, consist of learned AdaIN layer, achieved by several strided convolutional layers followed by fully connected layers.

Original: The content generator has also several convolutional layers and one single transpose convolutional layer which doubles the spatial resolution (Figure 4(right)).
Revised: The content generator has also several convolutional layers and one single transpose convolutional layer which doubles the spatial resolution (Figure 4(right)).

Original: In practice we only train G 5 , G 4 , G 3 , and apply feature inversion directly on the output of the latter, with negligible degradation.
Revised: In practice we only train G 5 , G 4 , G 3 , and apply feature inversion directly on the output of the latter, with negligible degradation.

Original: For the exact layer’s speciﬁcs we refer the reader to Appendix A.1 and to our (soon to be published) code.
Revised: For the exact layer’s speciﬁcs we refer the reader to Appendix A.1 and to our (soon to be published) code.

Original: We run each layer for 400 epochs with ﬁxed learning rate of 0 .and
Revised: We run each layer for 400 epochs with ﬁxed learning rate of 0 .and

Original: Adam optimizer (Kingma & Ba, 2014).
Revised: Adam optimizer (Kingma & Ba, 2014).

Original: On a single RTX 2080 reaching the ﬁnal image takes around 2 .
Revised: On a single RTX 2080 reaching the ﬁnal image takes around 2 .

Original: 5 days, including the ﬁnal inversion network training.
Revised: 5 days, including the ﬁnal inversion network training.

Original: [Figure]

Original: [Figure]

Original: O r i g i n a l

Original: O u r s

Original: M UN I T c y c l e G AN

Original: D R I T G AN - i m o r ph Several translation examples are presented in Figure 5.
Revised: Several translation examples are presented in Figure 5.

Original: Our translation is able to achieve high shape deformation.
Revised: Our translation is able to achieve high shape deformation.

Original: Note that our translations are semantically consistent, in the sense that they preserve the pose of the object of interest, as well the number of instances.
Revised: Note that our translations are semantically consistent, in the sense that they preserve the pose of the object of interest, as well the number of instances is mostly preserved.

Original: Furthermore, partial occlusions of such objects, or their cropping by the image boundaries are correctly reproduced.
Revised: Furthermore, partial occlusions of such objects, or their cropping by the image boundaries are correctly reproduced.

Original: See for example, the translations of the pairs of animals in columns 5–6.
Revised: See for example, the translations of the pairs of animals in columns 5–6.

Original: More results are provided in Appendix A.2.
Revised: More results are provided in Appendix A.2.

Original: 4. A BLATION STUDY
Revised: 4. A BLATION STUDY

Original: We analyze two main elements of our method.
Revised: We analyze two main elements of our method.

Revised: Loss components

Original: First, we validate the use of CycleGAN loss components.
Revised: First, we validate the use of CycleGAN loss components.

Original: As shown in Figure 6, we translate the 5th (deepest) layer with and without cycle, identity and adversarial losses.
Revised: As shown in Figure 6, we translate the 5th (deepest) layer with and without cycle, identity and adversarial losses.

Original: The best approach is achieved by using all of the losses, which balance each other.
Revised: The best approach is achieved by using all of the losses, which balance each other.

Original: In addition, in Figure 7 we compare translation of different VGG-19 layers.
Revised: In addition, In Figure 7 we compare translation of different VGG-19 layers.

Original: Evidently, shallower layers introduce spatial constraints, thus, limiting the translation in the sense of shape’s changes.
Revised: Evidently, shallower layers introduce spatial constraints, thus, limiting the translation in the sense of shape’s changes.

Original: The shallowest layer can hardly change the shape of the input image, which may explain the failure of traditional image translation methods.
Revised: The shallowest layer can hardly change the shape of the input image, which may explain the failure of traditional image translation methods.

Revised: In Table 1, we use the common FID score (Szegedy et al., 2015) to show that the cascaded translation achieves better translation compared to individual layer translation.

Original: Additional results are shown in Appendix A.
Revised: Additional results are shown in Appendix A.

Revised: Type of pre-trained network

Revised: While our method is conceptually agnostic to the type of features extraction network, we still require the extracted features to represent high-level semantic.

Revised: Therefore,

Revised: VGG-19 layers, which are the most common features used for image generation tasks (Aberman et al.

Original: 4. C OMPARISON TO OTHER METHODS
Revised: O r i g i n a l O u r s M UN I T c y c l e G AN D R

Revised: I T

Revised: G AN - i m o r ph

Revised: [Figure]

Revised: ; Dosovitskiy & Brox (2016); Gatys et al.

Revised: ; Liao et al.

Revised: (2017)), was chosen.

Revised: Nonetheless, we experiment with a ﬁne-tuned version of VGG-19 and different network architecture as shown in Figure 9.

Revised: We ﬁne-tuned VGG to classify between zebras and giraffes and trained our translation networks using the resulting features.

Revised: As can be seen, the translation results are inferior to the results achieved by the standard VGG-19.

Revised: This maybe attributed to VGG-19 ﬁxating on the unique differences between zebras and giraffes, unrelated to the translation, such as background.

Revised: For more about the extracted features, we refer the reader to Appendix A.1.3.

Original:  We compare our result with leading image translation methods, i.e. CycleGAN (Zhu et al., 2017),
Revised: In addition, in Figure 9, we examine a different network, AlexNet, also pretrained on ImageNet. We have observed that the deepest image translation was not able to generate a valid shape of zebra or giraffe. AlexNet is composed of 5 convolutional layers, where the ﬁrst one uses a stride of 4 . Therefore, the resulting features have less spatial encoding, especially at the deeper layers, which may explain the difﬁculty to invert and translate these features.

Original: MUNIT (Huang et al., 2018), DRIT (Lee et al., 2018) and GANimorph (Gokaslan et al., 2018).
Revised: [Table] [Table] [Figure]

Original: [Table]
Revised: [Table]

Original: [Table] [Figure]
Revised: We compare our result with leading image translation methods, i.e. CycleGAN (Zhu et al., 2017), MUNIT (Huang et al., 2018), DRIT (Lee et al., 2018) and GANimorph (Gokaslan et al., 2018).

Original: Quantitative comparison: In order to perform a quantitative comparison, we use the common FID score (Szegedy et al., 2015), as reported in Table 1.
Revised: Quantitative comparison: In order to perform a quantitative comparison, we use the common FID score (Szegedy et al., 2015), as reported in Table 2.

Original: Our method achieved the best FID score on ﬁve out of the six cross-domain translations for which this score was measured.
Revised: Our method achieved the best FID score on ﬁve out of the six cross-domain translations for which this score was measured.

Original: Qualitative comparison: In Figure 8 we show several challenging translation examples.
Revised: Qualitative comparison: In Figure 8 we show several challenging translation examples.

Original: While traditional image translation methods struggle to preform translations with such drastic shape deformation, our method is able to do so thanks to its use of the pre-trained VGG-19 network.
Revised: While traditional image translation methods struggle to preform translations with such drastic shape deformation, our method is able to do so thanks to its use of the pre-trained VGG-19 network.

Original: The success of our method can also be explained and visualized by examining the translated deep features.
Revised: The success of our method can also be explained and visualized by examining the translated deep features.

Original: We feed forward every image, original and translated, through the entire VGG network, extracting the last fully-connected layer (before the classiﬁcation layer).
Revised: We feed forward every image, original and translated, through the entire VGG network, extracting the last fully-connected layer (before the classiﬁcation layer).

Original: We project this vector (of size 4096 ) to 2D, using t-SNE, as shown in Figure 9.
Revised: We project this vector (of size 4096 ) to 2D, using t-SNE, as shown in Figure 9.

Original: It may be seen that the distribution of the translated vectors (in cyan) is closest to that of the target domain (in red) when using our method.
Revised: It may be seen that the distribution of the translated vectors (in cyan) is closest to that of the target domain (in red) when using our method.

Original: Limitations Our method achieves translations with signiﬁcant shape deformation in many previously unattainable scenarios, yet, a few limitations remain.
Revised: Limitations Our method achieves translations with signiﬁcant shape deformation in many previously unattainable scenarios, yet, a few limitations remain.

Original: First, the background of the object is not preserved, as the background is encoded in the deep features along with the semantic parts.
Revised: First, the background of the object is not preserved, as the background is encoded in the deep features along with the semantic parts.

Original: Also, in some cases the translated deep features may be missing small instances or parts of the object.
Revised: Also, in some cases the translated deep features may be missing small instances or parts of the object.

Original: This may be attributed to the fact that VGG-19 is generally not invertible and was trained to classify ﬁnite set of classes.
Revised: This may be attributed to the fact that VGG-19 is generally not invertible and was trained to classify ﬁnite set of classes.

Original: In addition, since we translate deep features, small errors in the deep translation may be ampliﬁed to large errors in the image, while for image-to-image translation method that operate on the image directly, small translation errors would typically be more local.
Revised: In addition, since we translate deep features, small errors in the deep translation may be ampliﬁed to large errors in the image, while for image-to-image translation method that operate on the image directly, small translation errors would typically be more local.

Revised: Please note that, similarly to CycleGAN and GANimorph, our translation is deterministic.

Original:  C ONCLUSIONS
Revised:  C ONCLUSIONS

Original: Translating between image domains that differ not only changes in appearance, but also exhibit signiﬁcant geometric deformations, is a highly challenging task.
Revised: Translating between image domains that differ not only changes in appearance, but also exhibit signiﬁcant geometric deformations, is a highly challenging task.

Original: We have presented a novel image-toimage translation scheme that operates directly on pre-trained deep features, where local activation patterns provide a rich semantic encoding of large image regions.
Revised: We have presented a novel image-toimage translation scheme that operates directly on pre-trained deep features, where local activation patterns provide a rich semantic encoding of large image regions.

Original: Thus, translating between such patterns is capable of generating signiﬁcant, yet semantically consistent, shape deformations.
Revised: Thus, translating between such patterns is capable of generating signiﬁcant, yet semantically consistent, shape deformations.

Original: In a sense, this solution may be thought of as transfer learning, since we make use of features that were trained for a classiﬁcation task for an unpaired translation task.
Revised: In a sense, this solution may be thought of as transfer learning, since we make use of features that were trained for a classiﬁcation task for an unpaired translation task.

Original: In the future, we would like to continue exploring the applications of powerful pre-trained deep features for other challenging tasks, possibly in different domains, such as videos, sketches or 3D shapes.
Revised: In the future, we would like to continue exploring the applications of powerful pre-trained deep features for other challenging tasks, possibly in different domains, such as videos, sketches or 3D shapes

Original: A A PPENDIX A. T RAINING DETAILS A.1. Hyper parameters
Revised: A A PPENDIX A. T RAINING DETAILS A.1. Hyper parameters

Original: In all our experiments, unless stated otherwise, we use Adam optimizer [16] with β 1 = 0 .
Revised: In all our experiments, unless stated otherwise, we use Adam optimizer [16] with β 1 = 0 .

Original: 5 , β 2 = 0 .
Revised: 5 , β 2 = 0 .

Original: The learning rate was set to 0 .
Revised: The learning rate was set to 0 .

Original: 0001 and the batch size to 10 .
Revised: 0001 and the batch size to 10 .

Original: During training, random crop and image mirroring is applied.
Revised: During training, random crop and image mirroring is applied.

Original: Our training methodology follows WGAN-GP (Gulrajani et al., 2017), thus for one generator update we update the discriminator four times.
Revised: Our training methodology follows WGAN-GP (Gulrajani et al., 2017), thus for one generator update we update the discriminator four times.

Original: A.1. N ETWORK ARCHITECTURE
Revised: A.1. N ETWORK ARCHITECTURE

Original: Feature Inversion Our implementation is similar to Dosovitskiy & Brox (2016).
Revised: Feature Inversion Our implementation is similar to Dosovitskiy & Brox (2016).

Original: We train an individual feature inversion network for VGG layer, where each layer has different channels ( 512 , 512 , 256 , 128 , 64 ).
Revised: We train an individual feature inversion network for VGG layer, where each layer has different channels ( 512 , 512 , 256 , 128 , 64 ).

Original: All layers utilize Leaky ReLU nonlinearity ( 0 . 2 ) and employ no normalization.
Revised: All layers utilize Leaky ReLU nonlinearity ( 0 . 2 ) and employ no normalization.

Original: The last layer utilizes Tanh.
Revised: The last layer utilizes Tanh.

Original: All inversion networks, ﬁrst apply three non-strided convolutional layers, with N input number of channels, equal to the number of channels each deep layer has.
Revised: All inversion networks, ﬁrst apply three non-strided convolutional layers, with N input number of channels, equal to the number of channels each deep layer has.

Original: Next, several transpose convolutional layers are applied, each doubles the resolution of the image and decreases the channel resolution (by factor of 2 ) until the image resolution 224 is achieved (thus, different amount of ConvTranspose layers per layer).
Revised: Next, several transpose convolutional layers are applied, each doubles the resolution of the image and decreases the channel resolution (by factor of 2 ) until the image resolution 224 is achieved (thus, different amount of ConvTranspose layers per layer).

Original: The ﬁnal layer is a non-strided convolutional layer followed by Tanh layer.
Revised: The ﬁnal layer is a non-strided convolutional layer followed by Tanh layer.

Original: Together they project the features back to the original image dimensions and range (number of output channels is 3 ).
Revised: Together they project the features back to the original image dimensions and range (number of output channels is 3 ).

Original: For the discriminator we have used Patch GAN discriminator, with four strided convolutional layers, each utilizes batch normalization (except the ﬁrst one) and Leaky
Revised: For the discriminator we have used Patch GAN discriminator, with four strided convolutional layers, each utilizes batch normalization (except the ﬁrst one) and Leaky

Original: ReLU.
Revised: ReLU.

Original: For the adversarial metric, only here, we have used LS-GAN.
Revised: For the adversarial metric, only here, we have used LS-GAN.

Original: Here we also set the batch size to 25 .
Revised: Here we also set the batch size to 25 .

Original: Deepest layer translation The input to the deepest translation network is conv_5_1 , thus, the input size is 14 × 14 × 512 (recall the input image size is 224 × 224 ).
Revised: Deepest layer translation The input to the deepest translation network is conv_5_1 , thus, the input size is 14 × 14 × 512 (recall the input image size is 224 × 224 ).

Original: The identity and cycle losses are multiplied by λ idty = λ cyc = 100 .
Revised: The identity and cycle losses are multiplied by λ idty = λ cyc = 100 .

Original: The architecture is reported in Table.2.
Revised: The architecture is reported in Table.3.

Original: The networks is relatively small and achieve good results in a few hours on a single GPU (RTX2080).
Revised: The networks is relatively small and achieve good results in a few hours on a single GPU (RTX2080).

Original: We use the
Revised: We use the

Original: WGAN-GP optimization method, updating the generator once for every four discriminator updates.
Revised: WGAN-GP optimization method, updating the generator once for every four discriminator updates.

Original: [Table]
Revised: [Table]

Original: Coarse to ﬁne conditional translation The coarse-to-ﬁne generator, for generating level i , has two inputs: the current source VGG level and the previous translated VGG features ( i + 1 ).
Revised: Coarse to ﬁne conditional translation The coarse-to-ﬁne generator, for generating level i , has two inputs: the current source VGG level and the previous translated VGG features ( i + 1 ).

Original: An
Revised: An

Original: AdaIN component, acts on on the current deep features and normalizes several layers in the translator itself.
Revised: AdaIN component, acts on on the current deep features and normalizes several layers in the translator itself.

Original: We report the AdaIN component structure for generating layer four in Table.3.
Revised: We report the AdaIN component structure for generating layer four in Table.4.

Original: The architecture can be extended easily to other layers.
Revised: The architecture can be extended easily to other layers.

Original: The core components of the translator, which takes as input the previous translated layer, are reported in Table.4.
Revised: The core components of the translator, which takes as input the previous translated layer, are reported in Table.5. [Table] [Table]

Revised: A.1. D IFFERENT NETWORKS

Original: [Table]
Revised: [Table]

Revised: 19 ﬁne

Revised: [Table]

Revised: tuned We ﬁne-tuned

Original: [Table]
Revised: [Table]

Original: A. M ORE COMPARISON RESULTS
Revised: -19 by ﬁxing all layers but the fully connected layers and conv_5_i ( i = 1 , 2 , 3 , 4 ). We replaced the ﬁnal fully connected layer with new fully connected layer of size 4096 × 2 corresponding to the two domains. We trained the classiﬁer for 50 epochs, with batch size of 25 and learning rate of 0 . 01 . AlexNet We extract each of the 5 convolution of AlexNet as different layers.

Revised: Each layer was normalized, in a similar manner as was described for VGG-19.

Revised: A. M ORE COMPARISON RESULTS

Original: In this section we show more results, not presented in the paper, for zebra ↔ giraffe, zebra ↔ elephant and cat ↔ dog translations.
Revised: In this section we show more results, not presented in the paper, for zebra ↔ giraffe, zebra ↔ elephant and cat ↔ dog translations.

Original: Original
Revised: Original

Original: Cycle GAN
Revised: Cycle GAN

Original: MUNIT
Revised: MUNIT

Original: DRIT
Revised: DRIT

Original: GANimorph
Revised: GANimorph

Revised: Ours

Revised: Original

Revised: Cycle GAN

Revised: MUNIT

Revised: DRIT

Revised: GANimorph

Revised: Ours

Revised: Original

Revised: Cycle GAN

Revised: MUNIT

Revised: DRIT

Revised: GANimorph

Revised: Ours

Revised: Original

Revised: Cycle GAN

Revised: MUNIT

Revised: DRIT

Revised: GANimorph

Revised: Ours

Revised: Original

Revised: Cycle GAN

Revised: MUNIT

Revised: DRIT

Revised: GANimorph

Original: Ours
Revised: Ours

Original: Original
Revised: Original

Original: Cycle GAN
Revised: Cycle GAN

Original: MUNIT
Revised: MUNIT

Original: DRIT
Revised: DRIT

Original: GANimorph
Revised: GANimorph

Original: Ours
Revised: Ours

Original: Original
Revised: Original

Original: Cycle GAN
Revised: Cycle GAN

Original: MUNIT
Revised: MUNIT

Original: DRIT
Revised: DRIT

Original: GANimorph
Revised: GANimorph

Original: Ours
Revised: Ours

Original: Original
Revised: Original

Original: Cycle GAN
Revised: Cycle GAN

Original: MUNIT
Revised: MUNIT

Original: DRIT
Revised: DRIT

Original: GANimorph
Revised: GANimorph

Original: Ours
Revised: Ours

Original: Original
Revised: Original

Original: Cycle GAN
Revised: Cycle GAN

Original: MUNIT
Revised: MUNIT

Revised: DRIT

Revised: GANimorph

Revised: Ours

Revised: Original

Revised: Cycle GAN

Revised: MUNIT

Original: DRIT
Revised: DRIT

Original: GANimorph
Revised: GANimorph

Original: Ours
Revised: Ours

Original: Original
Revised: Original

Original: Cycle GAN
Revised: Cycle GAN

Original: MUNIT
Revised: MUNIT

Original: DRIT
Revised: DRIT

Original: GANimorph
Revised: GANimorph

Original: Ours
Revised: Ours

Original: Original
Revised: Original

Original: Cycle GAN
Revised: Cycle GAN

Original: MUNIT
Revised: MUNIT

Original: DRIT
Revised: DRIT

Original: GANimorph
Revised: GANimorph

Original: Ours

Original: Original

Original: Cycle GAN

Original: MUNIT

Original: DRIT

Original: GANimorph

Original: Ours

Original: Original

Original: Cycle GAN

Original: MUNIT

Original: DRIT

Original: GANimorph

Original: Ours

Original: Original

Original: Cycle GAN

Original: MUNIT

Original: DRIT

Original: GANimorph

Original: Ours

Original: Original

Original: Cycle GAN

Original: MUNIT

Original: DRIT

Original: GANimorph

Original: Ours

Original: Original

Original: Cycle GAN

Original: MUNIT

Original: DRIT

Original: GANimorph

Original: Ours
Revised: Ours

Original: A. N ON - SHAPE DEFORMATION TRANSLATION
Revised: A. N ON - SHAPE DEFORMATION TRANSLATION

Original: Our method is also suited to none-shape deformation tasks, as in the case of dataset(1)
Revised: Our method is also suited to none-shape deformation tasks, as in the case of dataset(1)

Original: (Lee et al., 2018).
Revised: (Lee et al., 2018).

Original: Original
Revised: Original

Original: Ours
Revised: Ours

Original: Original
Revised: Original

Original: Ours
Revised: Ours

Original: Original
Revised: Original

Original: Ours
Revised: Ours

Original: Original
Revised: Original

Original: Ours
Revised: Ours

Original: Original
Revised: Original

Original: Ours
Revised: Ours

Original: Original
Revised: Original

Original: Ours
Revised: Ours

Original: A. C OARSE TO FINE TRANSLATION
Revised: A. C OARSE TO FINE TRANSLATION

Original: We here present the translation of each layer.
Revised: We here present the translation of each layer.

Original: The translation of each shallower layer is conditioned on the translation result of the previous layer, and learns to add ﬁne scale and appearance, such as texture.
Revised: The translation of each shallower layer is conditioned on the translation result of the previous layer, and learns to add ﬁne scale and appearance, such as texture.

Original: At every layer, in order to visualize the generated deep features, we use a network pre-trained for inverting the deep features of VGG-19, following the method of Dosovitskiy & Brox (2016).
Revised: At every layer, in order to visualize the generated deep features, we use a network pre-trained for inverting the deep features of VGG-19, following the method of Dosovitskiy & Brox (2016).

Original: A. N EAREST NEIGHBOR COMPARISON
Revised: A. N EAREST NEIGHBOR COMPARISON

Original: In this section we show side by side, source images, our translation and the three nearest neighbors in the target domain.
Revised: In this section we show side by side, source images, our translation and the three nearest neighbors in the target domain.

Original: We use the LPIPS metric, presented in Zhang et al.
Revised: We use the LPIPS metric, presented in Zhang et al.

Original: This metric is based on L 2 distance of deep features extracted from pre-trained network.
Revised: This metric is based on L 2 distance of deep features extracted from pre-trained network.

Original: In our case we use the default settings proposed by Zhang et al.
Revised: In our case we use the default settings proposed by Zhang et al.

Original: (i.e. alex net).
Revised: (i.e. alex net).

Original: As we show, the closest image in the target dataset vary in pose, scale and content (i.e. different parts of the objects).
Revised: As we show, the closest image in the target dataset vary in pose, scale and content (i.e. different parts of the objects).

Original: [Figure]
Revised: [Figure]

Original: Original
Revised: Original

Original: NN
Revised: NN

Original: NN NN
Revised: NN NN

Original: Ours
Revised: Ours