Original: E NJOY Y OUR E DITING : C ONTROLLABLE GAN S FOR I MAGE E DITING VIA L ATENT S PACE N AVIGATION
Revised: E NJOY Y OUR E DITING : C ONTROLLABLE GAN S FOR I MAGE E DITING VIA L ATENT S PACE N AVIGATION

Original: Anonymous authors

Original: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: Controllable semantic image editing enables a user to change entire image attributes with  few clicks, e.g., gradually making a summer scene look like it was taken in winter.
Revised: Controllable semantic image editing enables a user to change entire image attributes with a few clicks, e.g., gradually making a summer scene look like it was taken in winter.

Original: Classic approaches for this task use a Generative Adversarial Net (GAN) to learn a latent space and suitable latent-space transformations.
Revised: Classic approaches for this task use a Generative Adversarial Net (GAN) to learn a latent space and suitable latent-space transformations.

Original: However, current approaches often suffer from attribute edits which are entangled, global image identity changes, and diminished photo-realism.
Revised: However, current approaches often suffer from attribute edits that are entangled, global image identity changes, and diminished photo-realism.

Original: To address these concerns, we learn multiple attribute transformations simultaneously,we integrate attribute regression into the training of transformation functions,  apply a content loss and an adversarial loss that encourage the maintenance of image identity and photo-realism.
Revised: To address these concerns, we learn multiple attribute transformations simultaneously, integrate attribute regression into the training of transformation functions, and apply a content loss and an adversarial loss that encourages the maintenance of image identity and photo-realism.

Original: We propose quantitative evaluation strategies for measuring controllable editing performance, unlike prior work which primarily focuses on qualitative evaluation.
Revised: We propose quantitative evaluation strategies for measuring controllable editing performance, unlike prior work, which primarily focuses on qualitative evaluation.

Original: Our model permits better control for both single- and multiple-attribute editing, whilealso preserving image identity and realism during transformation.
Revised: Our model permits better control for both single- and multipleattribute editing while preserving image identity and realism during transformation.

Original: We provide empirical results for both real and synthetic images, highlighting that our model achieves state-of-the-art performance for targeted image manipulation.
Revised: We provide empirical results for both natural and synthetic images, highlighting that our model achieves state-of-the-art performance for targeted image manipulation.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Semantic image editing is the task of transforming a source image to a target image, while modifying desired semantic attributes, e.g., to make an image taken during summer look like it was captured in winter.
Revised: Semantic image editing is the task of transforming a source image to a target image while modifying desired semantic attributes, e.g., to make an image taken during summer look like it was captured in winter.

Original: The ability to semantically edit images is useful for various real-world tasks, including artistic visualization, design, photo enhancement, and targeted data augmentation.
Revised: The ability to semantically edit images is useful for various real-world tasks, including artistic visualization, design, photo enhancement, and targeted data augmentation.

Original: To this end, semantic image editing has two primary goals: (i) providing continuous manipulation of multiple attributes simultaneously and (ii) maintaining the original image’s identity as much as possible while ensuring photo-realism.
Revised: To this end, semantic image editing has two primary goals: (i) providing continuous manipulation of multiple attributes simultaneously and (ii) maintaining the original image’s identity as much as possible while ensuring photo-realism.

Original: Existing GAN-based approaches  can be categorized roughly into two groups: 1) image-space editing, i.e., methodsthat directly transform one image to another across domains (Choi et al., 2018; 2020; Isola et al., 2017; Lee et al., 2020; Wu et al., 2019; Zhu et al., 2017a;b), usually using variants of generative adversarial nets (GANs) (Goodfellow et al., 2014).
Revised: Existing GAN-based approaches for semantic image editing can be categorized roughly into two groups: (i) image-space editing methods directly transform one image to another across domains (Choi et al., 2018; 2020; Isola et al., 2017; Lee et al., 2020; Wu et al., 2019; Zhu et al., 2017a;b), usually using variants of generative adversarial nets (GANs) (Goodfellow et al., 2014).

Original: These approaches often have high computational cost, and they primarily focus on binary attribute (on/off) changes, rather than providing continuous attribute editing abilities.
Revised: These approaches often have high computational cost, and they primarily focus on binary attribute (on/off) changes, rather than providing continuous attribute editing abilities.

Original: 2) latent-space editing, i.e., methodsthat focus on discovering latent variable manipulations that permit continuous semantic image edits in a lower-dimensional space, most often using a GAN latent space.
Revised: (ii) latent-space editing methods focus on discovering latent variable manipulations that permit continuous semantic image edits. The chosen latent space is most often the latent space of GANs.

Original: Both unsupervised and (self-)supervised latent space editing methods have been proposed.
Revised: Both unsupervised and (self-)supervised latent space editing methods have been proposed.

Original: Unsupervised latent-space editing methods (H¨ark¨onen et al., 2020; Voynov & Babenko, 2020) are often less effective at providing semantic meaningful directions and all too often change image identity during an edit.
Revised: Unsupervised latent-space editing methods (H¨ark¨onen et al., 2020; Voynov & Babenko, 2020) are often less effective at providing semantically meaningful directions and all too often change image identity during an edit.

Original: Current (self-)supervised methods (Jahanian et al., 2019; Plumerault et al., 2020) are limited to geometric edits such as rotation and scale.
Revised: Current (self-)supervised methods (Jahanian et al., 2019; Plumerault et al., 2020) are limited to geometric edits such as rotation and scale.

Original: To our knowledge, only one supervised approach has been proposed (Shen et al., 2019) – developed to discover semantic latent-space directions for binary attributes.
Revised: To our knowledge, only one supervised approach has been proposed (Shen et al., 2019) – developed to discover semantic latent-space directions for binary attributes.

Original: As we show, this method suffers from entangled attributes and often does not preserve image identity during manipulation.
Revised: As we show, this method suffers from entangled attributes and often does not preserve image identity during manipulation.

Original: Contributions.
Revised: Contributions.

Original: We propose a latent-space editing framework for semantic image manipulation that fulﬁlls the aforementioned primary goals.
Revised: We propose a latent-space editing framework for semantic image manipulation that fulﬁlls the aforementioned primary goals.

Original: For this, we use a GAN and employ a joint sampling strategy, which is in turn trained to edit multiple attributes simultaneously.
Revised: Speciﬁcally, we use a GAN and employ a joint sampling strategy trained to edit multiple attributes simultaneously.

Original: To disentangle attribute transformationsin the latent space of GANs, we integrate a regressor that predicts the attributes that an image exhibits. The regressor also permits precise control of the degree of manipulation, and Original Inversion + Smile
Revised: To disentangle attribute transformations

Original: [Figure]
Revised: [Figure]

Original: Hair

Original: [Figure]

Original: Age

Original: [Figure] Hair Original Inversion + Night + Winter + Clouds + Sunny
Revised: Sunny

Original: B a s e li n e
Revised: B a s e li n e

Original: O u r s O u r s
Revised: O u r s O u r s

Original: B a s e li n e either changes image identity confounds is easily extended to multiple attributes simultaneously.
Revised: B a s e li n e in the latent space of GANs, we integrate a regressor to predict the attributes that an image exhibits. The regressor also permits precise control of the manipulation degree and is easily extended to multiple attributes simultaneously.

Original: In addition, we incorporate a perceptual loss (Li et al., 2019) and an adversarial loss that help preserve image identity and photo-realism during manipulation.
Revised: In addition, we incorporate a perceptual loss (Li et al., 2019) and an adversarial loss that helps preserve image identity and photo-realism during manipulation.

Original: We compare our method to several popular frameworks, from existing image-to-image translation methods (Choi et al., 2020; Wu et al., 2019; Zhu et al., 2017a) to latent space transformation-based approaches (Shen et al., 2019; Voynov & Babenko, 2020).
Revised: We compare our method to several popular frameworks, from existing image-to-image translation methods (Choi et al., 2020; Wu et al., 2019; Zhu et al., 2017a) to latent space transformation-based approaches (Shen et al., 2019; Voynov & Babenko, 2020).

Original: We mention that prior work primarily uses qualitative evaluation.
Revised: We mention that prior work primarily uses qualitative evaluation like the one in Fig.

Original: In contrast, we propose a quantitative evaluation to measure controllability .
Revised: In contrast, we propose a quantitative evaluation to measure controllability .

Original: Both qualitative and quantitative results provide evidence that our approach outperforms prior work in terms of quality of the semantic image manipulation, while maintaining image identity.
Revised: Both qualitative and quantitative results provide evidence that our approach outperforms prior work in terms of quality of the semantic image manipulation while maintaining image identity.

Original:  R ELATED WORK
Revised:  R ELATED WORK

Original: Generative Adversarial Networks (GANs) (Goodfellow et al., 2014) have signiﬁcantly improved realistic image generation in recent years (Brock et al., 2018; Jolicoeur-Martineau, 2019; Karras et al., 2017; 2019a;b; Park et al., 2019; Zhang et al., 2018).
Revised: Generative Adversarial Networks (GANs) (Goodfellow et al., 2014) have signiﬁcantly improved realistic image generation in recent years (Brock et al., 2018; Jolicoeur-Martineau, 2019; Karras et al., 2017; 2019a;b; Park et al., 2019; Zhang et al., 2018).

Original: For this, a GAN formulates a 2-player non-cooperative game between two deep nets: (1) a generator that produces an image given a random noise vector  sampled from a known prior distribution, usually a normal or a uniform distribution; (2) a discriminator whose input is  synthetic and real data, which is to be differentiated.
Revised: For this, a GAN formulates a 2-player non-cooperative game between two deep nets: (i) a generator that produces an image given a random noise vector in the latent space, sampled from a known prior distribution, usually a normal or a uniform distribution; (ii) a discriminator whose input is both synthetic and real data, which is to be differentiated.

Original: Semantic image editing seeks to automate image manipulation of semantics.
Revised: Semantic image editing seeks to automate image manipulation of semantics.

Original: Encouraged by the success of deep nets, recent works have applied deep learning methods for semantic image editing tasks such as style transfer (Li et al., 2017; Luan et al., 2017), image-to-image translation (Choi et al., 2018; 2020; Isola et al., 2017; Lee et al., 2020; Wang et al., 2018; Wu et al., 2019; Zhu et al., 2017a) and discovering semantically meaningful directions in a GAN latent space (H¨ark¨onen et al., 2020;
Revised: Encouraged by the success of deep nets, recent works have applied deep learning methods for semantic image editing tasks such as style transfer (Li et al., 2017; Luan et al., 2017), image-to-image translation (Choi et al., 2018; 2020; Isola et al., 2017; Lee et al., 2020; Wang et al., 2018; Wu et al., 2019; Zhu et al., 2017a) and discovering semantically meaningful directions in a GAN latent space (H¨ark¨onen et al., 2020;

Original: Jahanian et al., 2019; Plumerault et al., 2020;
Revised: Jahanian et al., 2019; Plumerault et al., 2020;

Original: Shen et al., 2019; Voynov & Babenko, 2020).
Revised: Shen et al., 2019; Voynov & Babenko, 2020).

Original: Note that our task is an extended version of semantic image editing that requires more comprehensive control to satisfy user-desired operations.
Revised: Note that our task is an extended version of semantic image editing that requires more comprehensive control to satisfy user-desired operations.

Original: Therefore, most aforementioned approaches do not meetthe requirements.
Revised: Therefore, most aforementioned approaches do not meet

Original: Nonetheless, we categorize the approaches that are most relevant to our task into two groups: (A) image editing via manipulation in image space, and (B) image editing via latent space navigation.

Original: [Figure]
Revised: [Figure]

Original: Separatebinarysampling
Revised: Separatebinarysampling

Original: Jointcontinuoussampling
Revised: Jointcontinuoussampling

Original: [Figure]
Revised: [Figure]

Revised: the requirements.

Revised: Nonetheless, we categorize the approaches most relevant to our task into two groups: (i) image editing via manipulation in image space, and (ii) image editing via latent space navigation.

Original: Image-space editing using GANs directly manipulates an image for targeted editing.
Revised: Image-space editing using GANs directly manipulates an image for targeted editing.

Original: Early work (Isola et al., 2017) used GANs to implement semantic translations between two image domains with paired data, e.g., day to night.
Revised: Early work (Isola et al., 2017) used GANs to implement semantic translations between two image domains with paired data, e.g., day to night.

Original: Follow-up work focused on multi-modal (Bhattarai &
Revised: Follow-up work focused on multi-modal (Bhattarai &

Original: Kim, 2020; Choi et al., 2018; 2020; Lee et al., 2020; Wang et al., 2018; Wu et al., 2019) and unpaired image domain translation (Zhu et al., 2017a).
Revised: Kim, 2020; Choi et al., 2018; 2020; Lee et al., 2020; Wang et al., 2018; Wu et al., 2019) and unpaired image domain translation (Zhu et al., 2017a).

Original: In this case, they primarily consider binary (on/off) attribute changes, regardless of whether the process is dynamic, for example, day to night.
Revised: In this case, they primarily consider binary (on/off) attribute changes, regardless of whether the process is dynamic, for example, day to night.

Original: Latent-space editing via GANs has received an increasing amount of recent interest.
Revised: Latent-space editing via GANs has received an increasing amount of recent interest.

Original: Most prior work focused on identifying semantically meaningful directions in the latent space of GANs, so that shifting latent vectors towards these directions achieves the desired image manipulation.
Revised: Most prior work focused on identifying semantically meaningful directions in the latent space of GANs, so that shifting latent vectors towards these directions achieves the desired image manipulation.

Original: Recent papers found semantics in the latent space of GANs, such as color transformations and camera movements (Jahanian et al., 2019), or face attribute changes (Shen et al., 2019), such as smile.
Revised: Recent papers found semantics in the latent space of GANs, such as color transformations and camera movements (Jahanian et al., 2019), or face attribute changes (Shen et al., 2019), such as smile.

Original: Other work considered unsupervised methods (H ¨ ark ¨ onen et al., 2020; Voynov & Babenko, 2020) to discover interpretable latent space directions.
Revised: Other work considered unsupervised methods (H ¨ ark ¨ onen et al., 2020; Voynov & Babenko, 2020) to discover interpretable latent space directions.

Original: However, additional challenges inherent to unsupervised manipulation of the latent space arise, and have not been addressed in prior work, e.g., direction quality with regard to degree control and image identity preservation.
Revised: However, additional challenges inherent to unsupervised manipulation of the latent space arise, and have not been addressed in prior work, e.g., direction quality with regard to degree control and image identity preservation.

Original:  M ETHOD 3. P ROBLEM S TATEMENT
Revised:  M ETHOD 3. P ROBLEM S TATEMENT

Original: We consider controllable semantic image editing via latent space navigation in GANs.
Revised: We consider controllable semantic image editing via latent space navigation in GANs.

Original: We begin with a ﬁxed GAN model that consists of a generator G and a discriminator D .
Revised: We begin with a ﬁxed GAN model that consists of a generator G and a discriminator D .

Original: The input of G is a latent vector z ∈ R m from a known distribution Z .
Revised: The input of G is a latent vector z ∈ R m from a latent space Z .

Original: Here, m is the dimensionality of the latent space.
Revised: Here, m is the dimensionality of the latent space.

Original: Given N attributes, we aim to discover semantically meaningful latent-space GAN directions,
Revised: Given N attributes, we aim to discover semantically meaningful latent-space GAN directions,

Original: T = { d 1 , . . .
Revised: T = { d 1 , . . .

Original: , d N } , to manipulate the attributes of synthetic images G ( z ) with an assigned step size ε = { ε 1 , . .
Revised: , d N } , to manipulate the attributes of synthetic images G ( z ) with an assigned step size ε = { ε 1 , . .

Original: ε N } , where d i ∈ R m for all i ∈ { 1 , . .
Revised: ε N } , where d i ∈ R m for all i ∈ { 1 , . .

Original: . , N } .
Revised: . , N } .

Original: In the end, each attribute of an edited image can be changed with the corresponding degree ε from α = { α 1 , . . .
Revised: In the end, each attribute of an edited image can be changed with the corresponding degree ε from α = { α 1 , . . .

Original: , α N } , the original attributes of G ( z ) predicted by a regressor R .
Revised: , α N } , the original attributes of G ( z ) predicted by a regressor R .

Original: 3. P ROPOSED APPROACH
Revised: 3. P ROPOSED APPROACH

Original: We provide an overview of our method in Fig.
Revised: We provide an overview of our method in Fig.

Original: 2 (a), and further illustrate how our approach differs from prior work in Fig.
Revised: 2 (a), and further illustrate how our approach differs from prior work in Fig.

Original: 2 (b-d).
Revised: 2 (b-d).

Original: Overview.
Revised: Overview.

Original: As shown in Fig.
Revised: As shown in Fig.

Original: 2 (a), we employ a GAN model consisting of a generator G and a discriminator D (not shown due to limited space), as well as a regressor R , all of which are pre-trained.
Revised: 2 (a), we employ a GAN model consisting of a generator G and a discriminator D (not shown due to limited space), as well as a regressor R , all of which are pretrained.

Original: Our goal is to ﬁnd latent directions T which provide attribute speciﬁc image edits.
Revised: Our goal is to ﬁnd latent directions T that provide attribute speciﬁc image edits.

Original: At each training step, G takes z and an edited latent vector referred to as z (cid:48) .
Revised: At each training step, G takes z and an edited latent vector referred to as z (cid:48) .

Original: We follow prior work which suggests that direction vectors in latent space of GANs permit image editing (Jahanian et al., 2019).
Revised: We follow prior work which suggests that direction vectors in latent space of GANs permit image editing (Jahanian et al., 2019).

Original: Formally, given a transformation degree ε , we obtain the latent-space edit as z (cid:48)
Revised: Formally, given a transformation degree ε , we obtain the latent-space edit as z (cid:48)

Original: =
Revised: =

Original: z + T ε =
Revised: z + T ε =

Original: z + (cid:80) Ni =
Revised: z + (cid:80) Ni =

Original: ε i d i .
Revised: ε i d i .

Original: G provides the recovered and edited images G ( z ) and G ( z (cid:48) ) , which are separately processed by the regressor R to predict attribute values.
Revised: G provides the recovered and edited images G ( z ) and G ( z (cid:48) ) , which are separately processed by the regressor R to predict attribute values.

Original: The original attribute of G ( z ) is α 
Revised: The original attributes of G ( z ) are α =

Original: = R ( G ( z )) .
Revised: R ( G ( z )) .

Original: The (pseudo-) ground truth and predicted attribute of G ( z (cid:48) ) are α (cid:48) and ˆ α (cid:48) , respectively, where α (cid:48)
Revised: The (pseudo-) ground truth and predicted attribute of G ( z (cid:48) ) are α (cid:48) and ˆ α (cid:48) , respectively, where α (cid:48)

Original: =
Revised: =

Original: α + ε and ˆ α (cid:48)
Revised: α + ε and ˆ α (cid:48)

Original: =
Revised: =

Original: R ( G ( z (cid:48) )) .
Revised: R ( G ( z (cid:48) )) .

Original: The goal of T is to transform z by adding semantically meaningful information such that the corresponding output image G ( z (cid:48) ) exhibits attribute changes ε from α .
Revised: Intuitively, the goal of T is to transform z by adding semantically meaningful information such that the corresponding output image G ( z (cid:48) ) exhibits attribute changes ε from α .

Original: In practice, we normalize the range of attribute values to [ 0 , 1 ] , i.e., both α and α (cid:48) ∈ [ 0 , 1 ] .
Revised: In practice, we normalize the range of attribute values to [ 0 , 1 ] , i.e., both α and α (cid:48) ∈ [ 0 , 1 ] .

Original: During training, we maintain the unit range by controlling the given ε .
Revised: During training, we maintain the unit range by controlling the given ε .

Original: Formally, ε is drawn from a distribution D ε uniform in [ − 1 , 1] N while considering the constraint that 0 ≤ α + ε ≤ 1 .
Revised: Formally, ε is drawn from a distribution D ε uniform in [ − 1 , 1] N while considering the constraint that 0 ≤ α + ε ≤ 1 .

Original: Objective function.
Revised: Objective function.

Original: To ﬁnd T we minimize the weighted objective: 
Revised: To ﬁnd T we minimize the weighted objective: min

Original: [Equation]
Revised: [Equation]

Original: The objective is only used for optimizing T , while the other modules remain ﬁxed.
Revised: Note, the objective is only used for optimizing T , while the other modules remain ﬁxed.

Original: Hyperparameters λ 1 , λ 2 , and λ 3 adjust the loss terms.
Revised: Hyperparameters λ 1 , λ 2 , and λ 3 adjust the loss terms.

Original: The regression loss L reg assesses whether T performs the transformations indicated by ε .
Revised: The regression loss L reg assesses whether T performs the transformations indicated by ε .

Original: We express the regression loss via a binary cross entropy:
Revised: We express the regression loss via a binary cross entropy:

Original: [Equation]
Revised: [Equation]

Original: Note that α (cid:48) is from the distribution generated by z and ε .
Revised: Note that α (cid:48) is from the distribution generated by z and ε .

Original: Please refer to the appendix C.1 for more details about this distribution.
Revised: Please refer to the appendix C.1 for more details on this distribution.

Original: The second loss term L disc is computed using the discriminator D and measures the quality of the generated images, i.e.,
Revised: The second loss term L disc is computed using the discriminator D and measures the quality of the generated images, i.e.,

Original: [Equation]
Revised: [Equation]

Original: Here we refer to the distribution of z (cid:48) via Z (cid:48)  conditioned on z , formally written as Z (cid:48) | z .
Revised: Here we refer to the domain of z (cid:48) via Z (cid:48) , which is conditioned on z . We write this using Z (cid:48) | z .

Original: Lastly, we use a content loss L content , often also referred to as perceptual loss.
Revised: Lastly, we use a content loss L content , often also referred to as perceptual loss.

Original: It is designed to estimate the distance between two images and it is employed to maintain the image identity during the transformation.
Revised: It is designed to estimate the distance between two images and it is employed to maintain the image identity during the transformation.

Original: For this we use the content loss term
Revised: Speciﬁcally, we use the content loss term

Original: [Equation]
Revised: [Equation]

Revised: [Equation]

Original: where F i ( · ) is a feature function that extracts intermediate features of images.
Revised: where F i ( · ) denotes a feature function which extracts intermediate features from images.

Original: D content indicates  layers being used from a pre-trained model.
Revised: D content indicates the layers of a pre-trained model which are used as features.

Original: We approximate the aforementioned expectations by empirical sampling.
Revised: We approximate the aforementioned expectations by empirical sampling.

Original: We defer algorithm details to the appendix A.
Revised: We defer algorithm details to Appendix A.

Original: Joint-distribution sampling and training.
Revised: Joint-distribution sampling and training.

Original: The regressor operates in a multi-label setting, i.e., each data possesses multiple attributes.
Revised: The regressor operates in a multi-label setting, i.e., each data possesses multiple attributes.

Original: To ensure the disentanglement of attribute edits we sample synthetic images from the entire data distribution and ﬁnd all transformations at once (illustrated in
Revised: To ensure the disentanglement of attribute edits we sample synthetic images from the entire data distribution and ﬁnd all transformations at once (illustrated in

Original: Fig.
Revised: Fig.

Original: 2 (b,c) right).
Revised: 2 (b,c) right).

Original: In contrast, Shen et al.
Revised: In contrast, Shen et al.

Original: (2019) prepare training samples on the two opposing data subsets with regards to an attribute, e.g., no clouds vs .
Revised: (2019) prepare training samples on the two opposing data subsets with regards to an attribute, e.g., no clouds vs .

Original: many clouds, and ﬁnd the directions with N one-vs-one classiﬁers (sketched in Fig.
Revised: many clouds, and ﬁnd the directions with N one-vs-one classiﬁers (sketched in Fig.

Original: 2 (b,c) left).
Revised: 2 (b,c) left).

Original: Transformation module T .
Revised: Transformation module T .

Original: We study two types of transformations T : i) global and ii) local .
Revised: We study two types of transformations T : (i) global and (ii) local .

Original: A global transformation T refers to a semantic latent-space transformation identical for all z during inference.
Revised: A global transformation T refers to a semantic latent-space transformation identical for all z during inference.

Original: This is illustrated via parallel red dashed arrows in Fig.
Revised: This is illustrated via parallel red dashed arrows in Fig.

Original: 2 (d) left.
Revised: 2 (d) left.

Original: The global directions  are commonly used (H¨ark¨onen et al., 2020; Jahanian et al., 2019;
Revised: These global directions [Figure] [Figure] are commonly used (H¨ark¨onen et al., 2020; Jahanian et al., 2019;

Original: Shen et al., 2019; Viazovetskyi et al., 2020).
Revised: Shen et al., 2019; Viazovetskyi et al., 2020).

Original: However, a globally identical direction might not serve all data.
Revised: However, a globally identical direction might not serve all data.

Original: In contrast, a local transformation T is a function of z which provides various directions for different z (in Fig.
Revised: In contrast, a local transformation T is a function of z which provides various directions for different z (in Fig.

Original: 2 (d) right, shown via non-parallel red arrows).
Revised: 2 (d) right, shown via non-parallel red arrows).

Original: Formally, d i = f iθ ( z ) , where f iθ is implemented via a deep net.
Revised: Formally, d i = f iθ ( z ) , where f iθ is implemented via a deep net.

Original: In Sec.
Revised: In Sec.

Original: 4 we show that the local transformation T succeeds on failure cases of the global transformation.[Figure] [Figure]
Revised: 4 we show that the local transformation T succeeds on failure cases of the global transformation.

Original:  E XPERIMENTS
Revised:  E XPERIMENTS

Original: We introduce datasets, implementation details, evaluation metrics, and show results next.
Revised: We introduce datasets, implementation details, evaluation metrics, and show results next.

Original: Datasets.
Revised: Datasets.

Original: We evaluate the proposed approach on two types of datasets: (i) face datasets –
Revised: We evaluate the proposed approach on two types of datasets: (i) face datasets –

Original: FFHQ (Karras et al., 2019a), CelebA (Liu et al., 2018) and CelebA-HQ (Karras et al., 2017), commonly used in prior work (H¨ark¨onen et al., 2020; Karras et al., 2017; 2019a;b; Shen et al., 2019;
Revised: FFHQ (Karras et al., 2019a), CelebA (Liu et al., 2018) and CelebA-HQ (Karras et al., 2017), commonly used in prior work (H¨ark¨onen et al., 2020; Karras et al., 2017; 2019a;b; Shen et al., 2019;

Original: Viazovetskyi et al., 2020).
Revised: 2014) and

Original: (ii) natural scene datasets – Transient Attribute Database (Laffont et al., 2014) and MIT Places2 data (Zhouet al., 2017), which contain attributes suitable for continuous semantic image editing.
Revised: (Zhou al., 2017), which contain attributes suitable for continuous semantic image editing.

Original: We brieﬂy introduce the scene datasets:
Revised: We brieﬂy introduce the scene datasets:

Original: • Transient Attribute Database (Laffont et al., 2014):
Revised: • Transient Attribute Database (Laffont et al., 2014):

Original: It contains 8,571 scene images with 40 attributes in 5 categories including lighting (e g., “bright”), weather (e.g., “cloudy”) , seasons (e g., “winter”), subjective impressions (e g., “beautiful”), and additional attributes (e
Revised: It contains 8,571 scene images with 40 attributes in 5 categories including lighting (e.g., “bright”), weather (e.g., “cloudy”) , seasons (e.g., “winter”), subjective impressions (e.g., “beautiful”), and additional attributes (e

Original: g., “dirty”).
Revised: g., “dirty”).

Original: Each attribute is annotated with a real-valued score between 0 and 1 , where 0 indicates absence of the attribute.
Revised: Each attribute is annotated with a real-valued score between 0 and 1 , where 0 indicates absence of the attribute.

Original: [Figure] • MIT Places2 data (Zhou et al., 2017): With the provided category annotations, we select the natural outdoor scenes, obtaining a total of 144,543 images.
Revised: • MIT Places2 data (Zhou et al., 2017): Using the provided category annotations (i.e., indoor/outdoor and natural/artiﬁcial), we select the natural outdoor scenes, obtaining a total of 144,543 images.

Original: Implementation details.
Revised: Implementation details.

Original: We choose λ 1 = 10 , λ 2 = 0 .
Revised: We choose λ 1 = 10 , λ 2 = 0 .

Original: 05 , λ
Revised: 05 , λ

Original: 3 = 0 .
Revised: 3 = 0 .

Original: 05 in Eq.
Revised: 05 in Eq.

Original: (1) and compute the perceptual loss using the conv 1 2 , conv 2 2 , conv 3 2 , conv 4 2 activations in a VGG-19 network (Simonyan & Zisserman, 2014)
Revised: (1) and compute the perceptual loss using the conv 1 2 , conv 2 2 , conv 3 2 , conv 4 2 activations in a VGG-19 network (Simonyan & Zisserman, 2014)

Original: pre-trained on the ImageNet dataset (Russakovsky et al., 2015).
Revised: pre-trained on the ImageNet dataset (Russakovsky et al., 2015).

Original: We train T for 50k iterations with a batch size of 4.
Revised: We train T for 50k iterations with a batch size of 4.

Original: An Adam optimizer is used with a learning rate of 1 e − 4 .
Revised: An Adam optimizer is used with a learning rate of 1 e-4.

Original: We evaluate the proposed method on the W space of StyleGAN2 and the Z space of PGGAN.
Revised: For the latent space Z in the proposed method we use the W space of StyleGAN2 and the Z space of PGGAN.

Original: For the attribute regressor R , we adopt a ResNet-

Revised: For the attribute regressor R , we adopt a ResNet-

Original: (He et al., 2016) for attribute prediction on the Transient Attribute (Laffont et al., 2014) and the CelebA (Liu et al., 2018) data.
Revised: (He et al., 2016) for attribute prediction on the Transient Attribute (Laffont et al., 2014) and the CelebA (Liu et al., 2018) data.

Original: The last fully connected layer in the ResNet-50 is replaced by a linear layer with an output dimension of 40.
Revised: The last fully connected layer in the ResNet-50 is replaced by a linear layer with an output dimension of 40.

Original: We train the regressors for 500 epochs and use the weights with the best validation mean squared error (MSE) on CelebA and the best test MSE on the Transient Scene Database.
Revised: We train the regressors for 500 epochs and use the weights with the best validation mean squared error (MSE) on CelebA and the best test MSE on the Transient Scene Database.

Original: TheGAN follows the
Revised: The

Original:  StyleGAN2 architecture and is pretrained with 200k iterations on a union of the two natural scene datasets using a resolution of 256 × 256 .
Revised: GAN follows the StyleGAN2 architecture and is pretrained with 200k iterations on a union of the two natural scene datasets using a resolution of 256 × 256 .

Original: The training batch size is 32 and an Adam optimizer is used with a learning rate of 2 e − 3 .
Revised: The training batch size is 32 and an Adam optimizer is used with a learning rate of 2 e-3.

Original: For the face datasets, we use FFHQ pre-trained weights 1 of StyleGAN2, and CelebA-HQ weights of PGGAN 2 .
Revised: For the face datasets, we use FFHQ pre-trained weights 1 of StyleGAN2, and CelebA-HQ weights of PGGAN 2 .

Original: Baselines.
Revised: Baselines.

Original: We compare our approach to several popular image-to-image translation approaches (Choi et al., 2020; Wu et al., 2019; Zhu et al., 2017a) and latent space direction discovery methods (Shen et al., 2019; Voynov & Babenko, 2020).
Revised: We compare our approach to several popular image-to-image translation approaches (Choi et al., 2020; Wu et al., 2019; Zhu et al., 2017a) and latent space direction discovery methods (Shen et al., 2019; Voynov & Babenko, 2020).

Original: Since Choi et al.
Revised: Since Choi et al.

Original: ; Shen et al.
Revised: ; Shen et al.

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

Original: (2017a) cannot deal with continuous attributes, we split the data into 2 domains using a threshold value of 0 .
Revised: (2017a) cannot deal with continuous attributes, we split the data into 2 domains using a threshold value of 0 .

Original: 5 for each of the 40 attributes.
Revised: 5 for each of the 40 attributes.

Original: Afterward, the models are trained on 2 contrast domains, e.g., with or without a certain attribute.
Revised: Afterward, the models are trained on 2 contrast domains, e.g., with or without a certain attribute.

Original: We use the ofﬁcial code of all baseline methods and rigorously follow their training steps.
Revised: We use the ofﬁcial code of all baseline methods and rigorously follow their training steps.

Original: Note that work by Voynov & Babenko (2020) is unsupervised, i.e., the latent-space directions are human interpreted.
Revised: Note that work by Voynov & Babenko (2020) is unsupervised, i.e., the latent-space directions are human interpreted.

Original: To avoid bias during the selection of directions, we use the attribute regressor to automatically identify the most signiﬁcant directions that can edit predetermined attributes.
Revised: To avoid bias during the selection of directions, we use the attribute regressor to automatically identify the most signiﬁcant directions that can edit predetermined attributes.

Original: The details of preparing the baselines are  in the appendix C.2.
Revised: The details of preparing the baselines are given in Appendix C.2.

Original: Evaluation metrics.
Revised: Evaluation metrics.

Original: There is no good numerical metric to evaluate image editing (Shen et al., 2019; Voynov & Babenko, 2020).
Revised: There is no good numerical metric to evaluate image editing (Shen et al., 2019; Voynov & Babenko, 2020).

Original: In an attempt to address this concern, we automate quantitative evaluation based on a property that editing of attributes should maintain image identity.
Revised: In an attempt to address this concern, we automate quantitative evaluation based on a property that editing of attributes should maintain image identity.

Original: To achieve this, we employ a popular image identity recognition model 3 pre-trained on the VGGface2 data (Cao et al., 2018).
Revised: To achieve this, we employ a popular image identity recognition model 3 pre-trained on the VGGface2 data (Cao et al., 2018).

Original: Cosine similarity is used to represent the similarity between paired original and edited images (Cao et al., 2018).
Revised: Cosine similarity is used to represent the similarity between paired original and edited images (Cao et al., 2018).

Original: In addition, we evaluate changes of the other independent attributes using the pre-trained regressor.
Revised: In addition, we evaluate changes of the other independent attributes using the pre-trained regressor.

Original: 4. R ESULTS ON NATURAL SCENE DATASETS
Revised: 4. R ESULTS ON NATURAL SCENE DATASETS

Original: Comparison to image-to-image translation: As shown in Fig.
Revised: Comparison to image-to-image translation: As shown in Fig.

Original: 3 (a-d), we observe image-to-image translation to perform poorly when editing image details (e.g., removing clouds) and sometimes [Figure]
Revised: 3 (a-d), we observe image-to-image translation to perform poorly when editing image details (e.g., removing clouds).

Original: contains artifacts (orange spots in (a) and (b)).
Revised: Moreover, sometimes artifacts (orange spots in (a) and (b)) are introduced.

Original: In contrast, our model shows overall compelling transfer performance on all these attributes.
Revised: In contrast, our model shows overall compelling transfer performance on all these attributes.

Original: We show more results in the appendix D.2.

Original: Comparison to latent-space translation: Inspecting Fig.
Revised: Comparison to latent-space translation: Inspecting Fig.

Original: 1, we observe our approach to improve upon work by Shen et al.
Revised: 1, we observe our approach to improve upon work by Shen et al.

Original: (2019) in attribute edit quality and number of artifacts.
Revised: (2019) in attribute edit quality and number of artifacts.

Original: Similarly, in Fig.
Revised: Similarly, in [Figure]

Original:  4, we notice that our method performs well in attribute editing, and has a better ability to preserve image identity when adding “night” and “snow.”
Revised: [Figure] Fig. 4, we notice that our method performs well in attribute editing, and has a better ability to preserve image identity when adding “night” and “snow.”

Original: We show more results in the appendix D.
Revised: We show additional analysis and results in Appendix D and E.1.

Original: Quantitative evaluation: We follow a user study proposed by Kowalski et al.
Revised: Quantitative evaluation: We follow a user study proposed by Kowalski et al.

Original: (2020) to evaluate controllability of our scene edits.
Revised: (2020) to evaluate controllability of our scene edits.

Original: Concretely, given an image I =
Revised: Concretely, given an image I =

Original: G ( z ) , we generate paired images ( I + , I − ) that have opposing values for attribute n .
Revised: G ( z ) , we generate paired images ( I + , I − ) that have opposing values for attribute n .

Original: 40 users took the test to answer: (a) the presence and strength of the attribute n in I + , and (b) if I + and I − are identical when ignoring the attribute.
Revised: 40 users took the test to answer: (a) the presence and strength of the attribute n in I + , and (b) if I + and I − are identical when ignoring the attribute.

Original: Both types of questions have options on a 5-level scale from 0 (“not at all”) to 1 (“totally”).
Revised: Both types of questions have options on a 5-level scale from 0 (“not at all”) to 1 (“totally”).

Original: Ideally, the response of question (a) should be 1 and (b) is 0 .
Revised: Ideally, the response to question (a) should be 1 while we expect replies for (b) to be 0 .

Original: We test performance of image editing with “clouds”, “sunrise&sunset”, “night” and “snow” attributes.
Revised: We test performance of image editing with “clouds”, “sunrise&sunset”, “night” and “snow” attributes.

Original: A question example and statistical results over 50 image pairs are shown in Fig.
Revised: A question example and statistical results over 50 image pairs are shown in Fig.

Original: Statistical evidence supports our claim that the proposed approach performs well with regards to image edits while maintaining image identity.[Figure] [Equation] [Table]
Revised: Statistical evidence supports our claim that the proposed approach performs well with regards to image edits while maintaining image identity.

Original: [Table]

Original: [Figure]

Original: [Figure]

Original: 4. R ESULTS ON FACE DATASETS
Revised: 4. R ESULTS ON FACE DATASETS

Original: Manipulation results on StyleGAN2: The comparison on real images shown in Fig.
Revised: Manipulation results on StyleGAN2: The comparison on real images shown in Fig.

Original: 1 suggests that our method works well on the shown attribute edits.
Revised: 1 suggests that our method works well for attribute edits.

Original: Further, the synthetic image edit results in Fig.
Revised: Further, the synthetic image edit results in Fig.

Original: 4 indicate that our edits are disentangled, while the baselines unexpectedly add “glasses” when aging the face.
Revised: 4 indicate that our edits are disentangled, while the baselines unexpectedly add “glasses” when aging the face.

Original: To increase the task difﬁculty, we edit real images with multiple attribute changes simultaneously.
Revised: To increase the task difﬁculty, we edit real images with multiple attribute changes simultaneously.

Original: Results are summarized in Fig.
Revised: Results are summarized in Fig.

Original: 6, which highlights the controllability of our edits.
Revised: 6, which highlights the controllability of our edits.

Original: Quantitative evaluation: We measure the changing degrees of the other independent attributes and  image identity when editing attributes with various degrees ˆ ε .
Revised: Quantitative evaluation: We measure the changing degrees of the other independent attributes and the image identity when editing attributes with various degrees ˆ ε .

Original: Here ˆ ε is a predicted changing degree on a target attribute by R .
Revised: Here ˆ ε is a predicted changing degree on a target attribute by R .

Original: For a thorough comparison, we evaluate the performance on 3 segments according to the absolute value of ˆ ε , i.e., | ˆ ε | in the range of (0 , 0 . 3] , (0 . 3 , 0 . 6] and (0 . 6 , 0 . 9] .
Revised: For a thorough comparison, we evaluate the performance on 3 seg- [Table] [Table] [Table]

Revised: [Table]

Revised: [Table]

Revised: [Table]

Revised: [Table]

Revised: et al.

Revised: [Table]

Revised: ments according to the absolute value of ˆ ε , i.e., | ˆ ε | in the range of (0 , 0 . 3] , (0 . 3 , 0 . 6] and (0 . 6 , 0 . 9] .

Original: Evaluation on “smile,” “hair color,” and “smile+hair color” attributes are shown in Tab. 1 and Tab. 2.
Revised: Evaluation on “smile,” “hair color,” and “smile+hair color” attributes are shown in Tab. 1 and Tab. 2.

Original: The “hair color” attribute includes “blond” and “black” colors where we average the results on both cases.
Revised: The “hair color” attribute includes “blond” and “black” colors where we average the results on both cases.

Original: For multiple attribute editing, “smile + hair color,” we evaluate the case that both the two targeted attributes change within the | ˆ ε | range.
Revised: For multiple attribute editing, “smile + hair color,” we evaluate the case that both the two targeted attributes change within the | ˆ ε | range.

Original: We use around 1k original images, generate 10k edited images with regard to each target attribute, and repeat the experiment 3 times.
Revised: We use around 1k original images, generate 10k edited images with regard to each target attribute, and repeat the experiment 3 times.

Original: The averaged results and the standard deviations presented in Tab. 1 and Tab. 2 suggest that our model outperforms the baselines with regard to disentanglement and image identity preservation.
Revised: The averaged results and the standard deviations presented in Tab. 1 and Tab. 2 suggest that our model outperforms the baselines with regard to disentanglement and image identity preservation.

Original: Manipulation results on PGGAN:
Revised: Manipulation results on PGGAN:

Original: For PGGAN, we use local transformations parameterized by three-layer perceptrons with leaky ReLU activations.
Revised: For PGGAN, we use local transformations parameterized by three-layer perceptrons with leaky ReLU activations.

Original: We show the detailed MLP architecture in the appendix B.1.
Revised: We show the detailed MLP architecture in Appendix B.1.

Original: Editing results in Fig.
Revised: Editing results in Fig.

Original: 7 suggest that the proposed approach is able to continuously edit faces with various semantic attributes.
Revised: 7 suggest that the proposed approach is able to continuously edit faces with various semantic attributes.

Original: More results are shown in the appendix.

Original: We also study the differences between local and global directions in Fig.8.
Revised: We also study the differences between local and global directions in Fig.

Original: Particularly, a global T (in Fig.(a)) is a matrix where each column i is given by d i .
Revised: Particularly, a global T (in Fig. 11 (a)) is a matrix where each column i is given by d i .

Original: For a local T (in Fig.
Revised: For a local T (in Fig.

Original: 8 (b))
Revised: 11 (b))

Original: d i is given by a fully-connected layer with identical input and output dimensions.
Revised: d i is given by a fully-connected layer with identical input and output dimensions.

Original: Fig.
Revised: Fig.

Original: 8 (c) and (d) use the MLP components with the aforementioned structure.
Revised: 11 (c) and (d) use the MLP components with the aforementioned structure.

Original: We use L reg to train T in (c), while the total loss L is used for (d).
Revised: We use L reg to train T in (c), while the total loss L is used for (d).

Original: The results in Fig.
Revised: The results in Fig.

Original: 8 indicate that a local T , with either linear or MLP structure works well for face attribute edits.
Revised: 11 indicate that a local T , with either linear or MLP structure works well for face attribute edits.

Original: Moreover, our loss L stabilizes the training process of T . 
Revised: Moreover, our loss L stabilizes the training process of T . [Figure] [Figure]

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: We propose an effective approach to semantically edit images by transferring latent vectors towards meaningful latent space directions.
Revised: We propose an effective approach to semantically edit images by transferring latent vectors towards meaningful latent space directions.

Original: The proposed method enables continuous image manipulations with respect to various attributes.
Revised: The proposed method enables continuous image manipulations with respect to various attributes.

Original: Extensive experiments highlight that our model achieves state-ofthe-art performance for targeted image manipulation.
Revised: Extensive experiments highlight that the model achieves state-ofthe-art performance for targeted image manipulation.

Revised: Acknowledgements: This work is supported in part by NSF under Grant No. 1718221, 2008387, 1934986 and MRI #1725729, NIFA award 2020-67021-32799, UIUC, Samsung, Amazon, 3M and

Revised: Cisco Systems Inc. (Gift Award CG 1377144).

Revised: We thank Cisco for access to the Arcetri cluster.

Revised: We thank Amazon for EC2 credits.

Original: APPENDIX A. ALGORITHM
Revised: APPENDIX A. ALGORITHM

Original: The overall procedure of the proposed method in Algorithm 1, taking local transformation Tθ as an example.
Revised: The overall procedure of the proposed method in Algorithm 1, taking local transformation T as an example.

Original: Input: A pre-trained GAN with G , D , and input noise distribution z ∼ Z ; a pre-trained regressor
Revised: Input: A pre-trained GAN with G , D , and input noise distribution z ∼ Z ; a pre-trained regressor

Original: R for predictions on N attributes; an initialized Tθ ; a learning rate µ θ for T θ ; max iteration number M .
Revised: R for predictions on N attributes; an initialized T ; max iteration number M .

Original: 1: for iteration m = 1 , . .
Revised: 1: for iteration m = 1 , . .

Original: , M do 2:
Revised: , M do 2:

Original: Sample random noise z ∼ Z , and ε 3:
Revised: Sample random noise z ∼ Z , and ε 3:

Original: Compute internal attributes for a synthetic image, i.e., α = R ( G ( z )) 4:
Revised: Compute internal attributes for a synthetic image, i.e., α = R ( G ( z )) 4:

Original: Compute the actual shift value δ = CLIP ( α + ε , (0 , 1))
Revised: Compute the actual shift value δ = CLIP ( α + ε , (0 , 1))

Original: − α 5:
Revised: − α 5:

Original: Compute the transformed latent vector z (cid:48)
Revised: Compute the transformed latent vector z (cid:48)

Original: =
Revised: =

Original: z + Tθ ( z ) δ 6:
Revised: z + T ( z ) δ 6:

Original: Compute I (cid:48)
Revised: Compute I (cid:48)

Original: =
Revised: =

Original: G ( z (cid:48) ) , α (cid:48) =
Revised: G ( z (cid:48) ) , α (cid:48) =

Original: α + δ 7:
Revised: α + δ 7:

Original: Compute attribute predictions ˆ α (cid:48)
Revised: Compute attribute predictions ˆ α (cid:48)

Original: =
Revised: =

Original: R ( I (cid:48) ) 8:
Revised: R ( I (cid:48) ) 8:

Original: Compute the loss L 9:
Revised: Compute the loss L 9:

Original: Update θ ← θ − µ θ ∗ ∂ L ∂θ 10: end for
Revised: Update T 10: end for

Original: Return: Tθ
Revised: Return: T

Original: B. METHOD DETAILS B.1. METHOD ON PGGAN
Revised: B. METHOD DETAILS B.1. METHOD ON PGGAN

Original: We apply a multi-layer perceptron (MLP) to parameterize a latent space path on PGGAN.
Revised: We apply a multi-layer perceptron (MLP) to parameterize a latent space path on PGGAN.

Original: Fig.shows the MLP architecture that we use.
Revised: Fig.shows the MLP architecture that we use.

Original: linear,leaky

Original: [Equation] linear [Equation] linear,leaky ReLU (0.
Revised: linear,leaky ReLU (0.2) linear,linear,leaky ReLU (0.2)

Original: [Equation]
Revised: [Equation]

Original: 𝑧 𝑑 ! In practice, we ﬁnd that normalizing the direction vector d i helps preserve image identity during manipulation on PGGAN.
Revised: In practice, we ﬁnd that normalizing the direction vector d i helps preserve image identity during manipulation on PGGAN.

Original: Formally, the transformed latent vector can be written as
Revised: Formally, the transformed latent vector can be written as

Original: [Equation]
Revised: [Equation]

Original:  where λ is a weight adjusting the direction scale.
Revised: (5) where λ is a weight adjusting the direction scale.

Original: We set λ = 3 .
Revised: We set λ = 3 .

Original: C. EXPERIMENTAL DETAILS C.1 T HE EXPECTATION IN L REG
Revised: C. EXPERIMENTAL DETAILS C.1 T HE EXPECTATION IN L REG

Original: We deﬁne the regression loss L reg as
Revised: We deﬁne the regression loss L reg as

Original: [Equation]
Revised: [Equation]

Original: where α (cid:48) is from the distribution generated by z and ε .
Revised: where α (cid:48) is from the distribution generated by z and ε .

Original: We now discuss how we derive the distribution of α (cid:48) .
Revised: We now discuss how we derive the distribution of α (cid:48) .

Original: To begin, z is sampled from Z and ε is sampled from D ε .
Revised: To begin, z is sampled from Z and ε is sampled from D ε .

Original: The other variables are generated by z and ε :
Revised: The other variables are generated by z and ε :

Original: [Equation]
Revised: [Equation]

Original: As a result, the distribution of α (cid:48) is dependent on Z and D ε .
Revised: As a result, the distribution of α (cid:48) is dependent on Z and D ε .

Original: C.2 U NSUPERVISED LATENT - SPACE EDITS OF GAN S
Revised: C.2 U NSUPERVISED LATENT - SPACE EDITS OF GAN S

Original: Voynov & Babenko (2020) learn a matrix where each column is a direction.
Revised: Voynov & Babenko (2020) learn a matrix where each column is a direction.

Original: Yet, Voynov & Babenko (2020) require a human to interpret the learnt directions.
Revised: Yet, Voynov & Babenko (2020) require a human to interpret the learnt directions.

Original: To avoid bias during the selection of directions in  comparison, we use the pre-trained attribute regressor to automatically identify the most signiﬁcant directions.
Revised: To avoid bias during the selection of directions in our comparison, we use the pre-trained attribute regressor to automatically identify the most signiﬁcant directions.

Original: Concretely, we ﬁrst generate 100 images and edit them with target attributes of various degrees.
Revised: Concretely, we ﬁrst generate 100 images and edit them with target attributes of various degrees.

Original: Next, we use the regressor R on the edited images to predict attributes.
Revised: Next, we use the regressor R on the edited images to predict attributes.

Original: We choose for an attribute edit the direction on which all the edited images have the overall highest changing response on the target attribute.
Revised: We choose for an attribute edit the direction on which all the edited images have the overall highest changing response on the target attribute.

Original: D. ADDITIONAL RESULTS D. SCENE IMAGE EDITS ON STYLEGAN
Revised: D. A DDITIONAL A NALYSIS D.1 GAN INVERSION PERFORMANCE

Revised: We use an optimization-based GAN inversion method (Abdal et al., 2019) to ﬁnd optimal latent vectors that can best reconstruct the real images via the generator.

Revised: To examine the effect of GAN inversion performance on our approach, we terminated the GAN inversion approach at different training steps, i.e., 500 and 4,000 iterations.

Revised: We show averaged reconstruction MSE loss in Tab. 3.

Revised: In this case, we reconstructed 20 real face images and edited their “Smile” and the “Blond hair” attribute with 10 different degrees ε , i.e., 200 images in total for each attribute editing.

Revised: Quantitative evaluation on image identity preservation and numerical changes on the other semantically independent attributes for the reconstructed face images are given in Tab. 4.

Revised: The results in Tab.

Revised: 4 suggest that the performance of the GAN inversion method affects our method to some degree.

Revised: Visualized inversion and editing results are shown in Fig.

Revised: The qualitative results suggest that our method still works remarkably well on the worse inversion image.

Revised: [Table]

Revised: D.2 A BLATION STUDY

Revised: We conduct an ablation study with single- and joint-distribution training strategies in our method.

Revised: Speciﬁcally, single-distribution sampling refers to learn one attribute direction at a time.

Revised: In contrast, joint-distribution training means to train multiple attribute directions simultaneously.

Original: We show more results of our method on continuous scene image edits in Fig. 
Revised: Fig. 11 shows the visualized results with the two training strategies on the face and the scene dataset. We observe that the model with the single-distribution training strategy generates more unexpected changes as the manipulation degree is getting large, e.g., darker scene colors by the model trained for a single attribute, shown in Fig. 11 (b).

Original: The semantic edit attributes include “clouds” (Fig. 10), “brightness” (Fig. 11), “snow” (Fig. 12), and “summer” (Fig. 13).
Revised: [Table] [Figure]

Original: et al., 2019b) on the “clouds” attribute.
Revised: [Table]

Original: D. SCENE IMAGE EDITS COMPARED WITH IMAGE-TO-IMAGE TRANSLATION WORK 
Revised: [Figure] E. ADDITIONAL RESULTS E. SCENE IMAGE EDITS ON STYLEGAN

Original: We show more results for image-space editing methods in Fig.
Revised: We show more results of our method on continuous scene image edits in Fig.

Original: Editing of scene images with multiple attributes simultaneously is shown in Fig.
Revised: The semantically edited attributes include “clouds” (Fig. 12), “brightness” (Fig. 13), “snow” (Fig. 14)

Original: [Figure]
Revised: [Table]

Original: [Figure]
Revised: and “summer” (Fig. 15).

Original: D. FACE IMAGE EDITS
Revised: F. B ROADER I MPACT

Original: Editing of face images are shown in Fig.
Revised: From an application perspective [Table] our method is effective and efﬁcient with regard to image manipulation and photo-realism, which we hope will contribute to 2D and 3D controllable image editing tasks. Moreover

Original: 16 -Fig.
Revised: [Table] usage of deep nets to learn mappings between spaces is still not well understood, e.g., mappings between low-dimensional space and image space in classiﬁcation and generation tasks. We hope our method provides inspiration for representation learning and a ﬁrst step for a new view with regard to deep net interpretability. Obviously, we are aware of the dangers of automated image manipulation.

Original: The semantic edit attributes includes “gender” (Fig. 16) and “smile” (Fig. 17).
Revised: Similar to deepfake tasks whose aim is to produce fabricated images and videos that appear to be real, improper use of image manipulation approaches might raise negative issues with regard to information security, property, etc. Beyond that, edited image detection techniques Wang et al. (2019a;b) are proposed recently to avoid the aforementioned issues, which promotes growth in both domains. [Figure]