Generative Auto-Encoder: Controllable Synthesis with Disentangled Exploration
Authors: Yunhao Ge
Gan Xin
Zhi Xu
Yao Xiao
Yunkui Pang
Yining HE
Laurent Itti
Venue:  (2021)
Keywords: Generative autoencoder
disentangled representation learning
attribute controllable synthesis

Abstract: Autoencoders perform a powerful information compression framework with are construction loss and can be a regularization module in different tasks, which has no generative ability itself. We wondering if an autoencoder gains generative ability without using GAN and VAE based modification, which are two mature methods.   Here we propose a new method:  Disentanglement and ExplorationAutoencoder (DEAE), DEAE using a disentangle and exploration positive iteration achieve semantic controllable synthesis especially controllable mining the novel semantic value.  For instance, given only red, green, and blue object color while mining new object color expressions in the image domain. The encoder of DEAE first turn the input sample into a disentangled latent code, then explore the latent codespace by attribute oriented interpolation.  To encourage interpolated latent code successfully output a semantically meaningful sample by the decoder, we propose a regularization procedure by ’reuse’ encoder and constrain the output latent value which implicitly improves the quality of the interpolated sample. DEAE can become a generative model and synthesis semantic controllable samples by interpolating latent code, which can even synthesis novel attribute value never is shown in the original dataset. Experiments demonstrate how disentanglement and exploration can boost each other which empowers autoencoder generative ability. We also demonstrate that DEAE can improve the performance of downstream tasks compared with GANand VAE based generative model, especially in controllable data augmentation, dataset bias elimination (Fairness)

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GENERATIVE AUTO-ENCODER: CONTROLLABLE SYN- THESIS WITH DISENTANGLED EXPLORATION
Anonymous authors Paper under double-blind review
ABSTRACT
Autoencoders are a powerful information compression framework, but it lacks generative ability. We explore whether an autoencoder can gain generative ability without invoking GAN-based adversarial training, which notoriously hard train and control. VAE provides one solution by adding Gaussian prior knowledge that can be sampled for synthesis but faces a low-quality problem. One naive way is directly through exploration in latent space such as interpolation; however, randomly interpolating the latent code may wander out the acceptable distri- bution of the decoder, which leads to inferior output. Here propose new method: Disentangled Exploration Autoencoder (DEAE), which uses disentangled representation and regularization guarantee the validity of exploration in latent space and achieve controllable synthesis. The encoder of DEAE first turns the input sample into disentangled latent code, then explores the latent code space through directed interpolation. To aid the interpolated latent code in successfully outputting meaningful sample, after the decoder, we regularize the output by 'reusing' the encoder to force the obtained latent representation to maintain per- fect disentanglement, which implicitly improves the quality of the interpolated sample. The disentanglement and exploration can boost each other and form a positive loop that empowers DEAE's generative ability. Experiments demonstrate that DEAE can improve the performance of downstream tasks by synthesizing attribute- comb augmented samples. We also demonstrate that DEAE can help to eliminate dataset bias, which provides solution for faireess problems.
INTRODUCTION AND RELATED WORK
Autoencoders (Ballard (187)) usually map the samples from high dimension space to a latent, low-dimensional space with minimum information loss and employ regularization function to help downstream tasks. The compressed low-dimensional space provides a high-level and compact representation to help understand the original dataset (Berthelot et al. (2018)) ). It is interesting to analyze the relationship between the latent space and image space (Bengio al. (2013)). especially how to empower the autoencoder to obtain generative ability. Prior work exists which turns an autoencoder into generative model. These methods can be divided into types: Adversarial training and VAES. As is shown in Fig. [ (a), Makhzani et al.) (2013); iZhao et al. (2018) uses adversarial learning in latent space control the output of encoder which have similar distribution as the real data. Berttelltt et al. (2018) uses an adversarial regularizer to improve interpolation in autoencoders. Other methods (Sainburg al. (2018) merge adversarial learning and autoencoder by adding a discriminator to guide the quality of synthesized samples. However, adversarial based learning suffers from unstable training and synthesis that hard control (Gulrajani al. (2017). As is shown in Fig. (b), VAEs (Kingma & Welling (2014): Higgins et al. (2017); Chen et al. 2018
provide another solution by adding prior Gaussian constraints latent space, then using latent code sampling synthesize new samples. Zhang al. (2019) add latent distribution constraint, which yields sharper outputs. However, VABs suffer from low-quality problems and convergence difficulty.
Moreover, both types of methods struggle to achieve controllable semantic synthesis. Adversarial- based controllable synthesis is mostly achieved by interpreting the latent space by finding the boundary (Shen et al. (2020a); Yang et al. (2019)), which has a high cost; most of the controllable synthesis is then restricted to binary attribute values and one cannot precisely control the synthesized image without influencing the other attributes. VAE-based controllable synthesis achieved by disentangling

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Figure 1: Different methods for empowering autoencoder generative ability
the representation in latent space struggles with the problems of hard to control and inferior synthesis quality. We propose a different solution to empower precise attribute controllable synthesis ability on autoencoders: Disentangled Exploration Autoencoder (DEAE).
Direct exploration latent space, such as interpolation, is a naive way for autoencoders to gener- new samples. Hopefully, the synthesized samples by the decoder achieve semantically meaningful output. However, there are two obstacles to directly interpolating without constraints. First, the interpolated latent code may get beyond the boundary of the non-convex distribution that the pre-trained decoder can 'understand', especially when the training dataset is small. Second, even though the synthesized samples may have high quality, it's still hard precisely control the synthesized samples to change only the target attribute without influencing other attributes.
To solve the problem of interpolation and aid semantic controllable synthesis autoencoders, DEAE (Fig. (c)) first turns the latent code into an attribute disentangled representation after encoding, through disentangled representation learning (Ge et al. (2020)) can then directly interpolate along any dimension of the disentangled latent code to change specific semantic attributes of output samples. For example, if object color specified in the first 3 dimensions
the latent code, exploring new object colors is achieved by interpolating within these 3 dimensions. But how to make sure the synthesized samples are semantically meaningful? We propose to 'reuse' the encoder regularization, with the assumption that if the synthesized interpolated sample is semantically meaningful, it should be properly encoded back to the latent space by the encoder. Furthermore, because we only interpolate specific attribute in disentangled latent code, when encode the decoded synthesized sample back to latent code again, the resulting latent code for all other attributes should the same the original. Thus, our regularization procedure can 'reuse' the encoder and constrain the latent values, which implicitly improves the quality of the interpolated samples and achieves precise attribute-comte contrllae- synthesis.
More importantly, the disentangled representation and exploration procedure can help each other during training. the one hand, the disentangled representation in latent code makes the attribute- controllable exploration possible, and it is also the foundation of the regularization by reusing the encoder. On the other hand, the better quality of interpolated samples, the better the disentan- glement by the encoder. This positive loop is crucial for DEAE training. Compared with other GAN-based controllable synthesis methods like InterFaceGAN (Shen et al. (2020a)), Fader Networks (Lample et al. (2018)). ) and starGAN (Choi al. (2018) ). which can be treated the first exploration then control, our method reverses the procedure by first control then exploration.
Our contributions (i) Propose Disentangled Exploration Autoencoder (DEAE), which uses disentangled representation and interpolation to boost each other in latent space and achieve the interpolation-based attribute-controllablee synthesis, including synthesis for novel attribute values
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never seen in training. (ii) Demonstrate how DEAE can exploration to improve the comprehension of the bridge between disentangled latent space and original space, where precise movement in latent space results in changing specific attribute(s) to specific target value(s). (iii) Demonstrate that DEAF outperforms state-of-the-art alternatives for controllable image synthesis and can be used as a data augmentation method which can boost other downstream tasks. (iv) Demonstrate that DEAE can help eliminate dataset bias and improve the performance of downstream tasks.
DISENTANGLED EXPLORATION AUTOENCODER FOR ATTRIBUTE CONTROLLABLE SYNTHESIS
An autoencoder consists of two main structures: first an 'encoder', which takes æ E Rd as input and obtains a latent code = fz)) R Then a 'decoder', which maps the latent code to an approximate reconstruction è (2) of the input z. In this paper, we consider the situation that both encoder and decoder are neural networks with trainable parameters @ and é respectively. Given a multi-attribute dataset D, each sample x E D has m attributes and each attribute has different attribute values. Take the dSprites dataset Matthey et al. (2017) as an instance, color, shape, scale, orientation, and X. Y positions are different attributes; the shape attribute, for example, has three values: square, cllipse, and heart. Our goal is to controllable synthesize new samples attribute(s). For example, given one image, synthesize new images with different shapes while the other five attributes remain unmodified. The challenging part is constrain the other attributes to remain unmodified when modifying the target attribute.
specific
DEAE uses the following steps to achieve controllable synthesis. First, use the Group supervised learning Ge et al. 2020) method to create a preliminary disentangled representation in latent space, where different attributes are disentangled with corresponding latent dimensions (Sec. 2.1). Then, explore the disentangled latent space by interpolating and synthesizing. Then we 'reuse" the encoder to regularize the synthesized samples, which makes the disentangled representation and exploration help each other (Sec. 2.2). This positive loop helps creating perfect disentangled autoencoder, where controllable synthesis can be achieved by freely interpolating latent space (Sec. 2.3). Through perfect disentanglement, DEAE creates subspace Re=,, for each attribute a; in latent code, which provides a useful view to exploring how to precisely control synthesis for each attribute (Sec. 2.4).
2.1 PRELIMINARY DISENTANGLED AUTOENCODER BY GSL
We use the Group supervised learning framework Ge et al. (0200) to train an autoencoder with disentangled representation in latent space as our baseline model (GSL-AE), Here are the steps: First, we organize the original dataset as a MultiGraph, where each sample represented as a node, and we use edges to represent the attribute relationships between nodes: if two nodes have the same attribute, e.g., same object color, they are linked by an edge. There may be multiple edges between two nodes. Second, we use the MultiGraph to guide group supervised training: at each training step, we sample a group of samples which is a sub- MultiGGrrrr from the whole MultiGraph as input, swap the latent code of the overlap attribute(s) as long as there are edges between them. We use attribute swap loss and cycle swap loss (Ge et al. (2020)) to mine samples with attributes similar to those in the group. This achieves disentangled representation for the given dataset. This disentanglement can synthesize new samples by recombining latent codes from different source samples.
2.2 EXPLORATION BY INTERPOLATION AND DISENTANGLEMERT REGULARIZATION
The generative ability of GSL-AE is powerful, however, there are limitations because the decoder can only 'understand' seen attribute values: the recombined latent code does represent a new combination but each attribute value related dimensions, as a unit, is seen in other dataset samples. This limitation hinders interpolation-batt based synthesis because each interpolation in latent code can be treated as an 'unseen' attribute value, and the decoder may not be able to 'understand' this new attribute and turn it back to a new sample with proper semantic meaning. For example, in Fig. 3 (a), we want to change the background color of an input image by interpolating the bookgrounnd-ooo--eelaae (20 dim) in disentangled latent code. If the latent code is non-convex, during interpolation from the source sample (red point) the target sample (green point), the middle point may out of the boundary, which means the decoder might not successfully turn it onto a meaningful image with the requested background color. The result of GSL-AE in Fig. illlustrates this problem.
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Figure 3: Enlarging explainable region by interpolation and regularization
To solve the problem of non-convex latent space, we define a perfect disentanglement property in latent d, dimension space of the autoencoder. For encoder fo- if we specifically modify one attribute value of image x and get å. and after encoding we get latent code fo (a)) and fo- (2), then the difference between z and å should be zero for all latent dimensions except those that represent the modified attribute. Similarly, for decoder Iø-, the change in latent space should only influence the corresponding attribute expression in image space. If both the encoder and decoder satisfy the requirements, we claim the autoencoder has a perfect disentanglement property. During training, augment the original training set by randomly interpolating new attribute values that lie between the values the training set. The constraints of perfect disentanglement then enforce that the latent representation of every attribute understandable by the decoder, i.e., forces, in the limit of infinite interpolated samples, the disentangled latent representation of every attribute to be convex.
DEAE tries turn the non-convex latent space to convex for each attribute by 'reusing' the encoder to regularize the latent space of synthesized images. As shown in Fig. (b), after we pass the synthesized interpolated image through the encoder again and get the latent code, we force it be the same as the latent code of the input image, except for the interpolated attribute related range, by adding a reconstruction loss. This disentangled regularization helps the autoencoder 'complete' the understandable field of each disentangled latent space and turn the original non-convex space to convex space to achieve perfect disentanglement. If is possible that the reconstruction loss decreases to zero for given dataset augmented by many interpolated samples, then perfect disentanglement and convexification are achieved. After that, we can freely interpolate any specific attribute related dimensions to achieve controllable synthesis. Fig. DEAE results show the superiority of DEAE based on the proposed methods over other models.
The exploration of the latent space and disentangled representation of attributes in DEAE are dual tasks in the sense that one can help enhance the performance of the other.
2.3 TOWARDS CONTROLLABLE EXPLORATION DIRECTION
After obtaining the perfect disentangled convex latent space for each attribute, in addition to syn- thesizing new samples by random interpolation, want find some more precise and effective methods for autribute-controllan synthesis problems. Here we discuss two ways: precise target synthesis, and mining new attribute values.
Precise attribute synthesis means we want to change specific attribute from value A to value B with the desired trajectory. If treat the precise attribute synthesis as a 'movement" in attribute- related latent space, moving with the desired trajectory means we can control the speed of movement (most effective path) and the direction during movement.
Novel attribute value mining means finding new attribute values that we have never seen before during training. For instance, given only red, green, and blue object color images, can we synthesize new object colors such as purple, yellow, any other colors? Can we find an effective method that gives the highest probability to find new attribute values during exploration in latent space? To solve the problems above, we put forward the unit direction vector (UDV) for each attribute. In detail, we treat each attribute value as a binary semantic label (e.g., car or not car). We assume there exists hyperplane in the latent space serving the separation boundary (hen al. (2020b)), and the distance from a sample to this hyperplane is in direct proportion to its semantic score. So, we can train SVM to find this boundary and use the vector orthogonal to the boundary and towards the
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Figure 4: Towards controllable exploration direction
positive side to represent the UDV. Then precise attribute synthesis and to find new attribute values. Controlling attributes with UDVs is general method which can be used in most cases. However, for those attributes (object color) whose attribute value (blue color) has small intra-class variance (all blues look similar), their distribution can be seen as a Gaussian distribution, and we can use K-means (Likas al. (2003)) find the center of each object color and compute the mean of latent value for those cluster centers.
the UDVs or the combination of them to achieve
As is shown in Fig. Then we can solve the precise attribute synthesis problem, as is shown in Fig. (b). by moving from the start point, across the boundary, the target attribute value, by adding the UDV of the target attribute step by step. Fig. 4 (c) illustrates that we can move the attribute value to the z value of the cluster center directly combine the UDVs to discover new attribute values.
we can find the boundaries and UDVs by SVM for each attribute value.
distribution of attribute is Gaussian. Fig. (d) shows that we can
2.4 ARCHITECTURE AND OBJECTIVE FUNCTION
We use the same architecture of autoencoders as Ge et al. (2020). The encoder E is composed of two convolutional layers with stride 2, followed by residual blocks, followed by a convolutional layer with stride 2, followed by reshaping the response map to a vector, and finally two fully-connected layers to output 100-dim vector as the latent feature. The decoder D, symmetric to F, take the 100-dim vector as input and output synthesized samples. Besides the reconstruction loss L,, swap reconstruction loss Let and cycle swap reconstruction loss Lese which are same as GSL, we add regularization reconstruction loss Lee-
= fo();; == =o(go(((Z
where zni demotes the latent vector after we interpolate in attribute oi related dimension in latent vector z. re denotes the latent space after reusing the encoder to the synthesized image.
Lug = 1|22-i - 2aallla
2-as and 2 denote the all latent dimensions of and n respectively, except those that represent the modified attribute ai
The total loss function is:
C(F, D) = T,p >gTa + Aest Test + >veg Trrgg
(3)
Where Xary Aose, Areg control the relative importance of the loss terms.
3 EXPERIMENTS
We conduct 5 main experiments: (3.1) The performance of DEAE compared with baseline mod- els on attribute controllable and interpolation random synthesis task with different datasets. (3.2) Demonstrate how disentangled representation and exploration boost each other in DEAE with qualitative and quantitative experiments (3.3) Demonstrate the performance of DEAE to find the direction towards attribute value with UDV which achieves precise attribute synthesis, and how to combine UDVs targeting for mining new attribute values. (3.4) Demonstrate the performance of DEAE, as generative model, controllable augment small datasets and boost the performance of
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downstream tasks, such classification. (3.5) Demonstrate DEAE can disentangled represen- tation to eliminate the bias dataset and improve the performance of downstream tasks, which provides a solution for fairness decision problems. We use the following datasets explore our topic.
Fonts is a computer-generateed RGB image dataset. Each image contains an alphabet letter rendered using 5 independent generating attributes: letter identity, size, font color, background color and font. BaFD(Langned et al. (2010)): contains pictures of 67 models displaying emotional expressions taken 5 different camera angles simultaneously. There are attributes: identity, camera position (pose), and expression.
3.1
CONTROLLABLE SYNTHESIS PERFORMANCE
Attribute controllable synthesis task On the Fonts dataset, we train our DEAE and three baseline models: General Autoencoder (GAE), 3- Variational Autoencoder (3-VAE), and Autoencoder with Group supervised learning(GSL-AE) [Ge et al. (2020)).
Fig. 5 (a) shows the performance of attribute controllable synthesis on the background color, size and fonts given two images. The first three rows show synthesized images by randomly interpolating background color related dimensions in two samples. B- V-V can hardly synthesize images which differ only in background color. The synthesized images by GSL also change the size and font range while DEAE can synthesize high quality images that change only the background color attribute while keeping the other attributes unmodified. Size and fonts show similar performance.
On RaFD dataset, we train DEAE to show the performance controllable synthesis on identity and expression (Fig. 6). The results show that DEAE can achieve controllable synthesis the expression as well as the identity attribute, generating new expressions and identities between the two sources.
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Figure 6: DEAE controllable synthesis performance on RAFD dataset. Linear interpolation on the identity(top) and expression(bottom) attribute between the source image and the target image.
Interpolation based random synthesis task As is shown Fig. (b) we randomly interpolate in latent code for all latent attributes given two samples, the result shows DEAE can synthesize new samples by freely interpolating latent space.
3.2 DISENTANGLEMENT AND EXPLORATION BOOST EACH OTHER
To further explore that DEAE can turn the non-convex space to a convex space by disentangled exploration. We claim that the exploration synthesis and disentangled representation of attributes are dual tasks in the sense that one can help enhance the performance of the other. Intuitively, the
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regularization loss to improve the quality of interpolated samples also help the decoder to 'enlarge' the understandable"' range to turn non-convex latent space to convex.
We use quantitative experiments to prove it. Firstly, Fonts-V2 V2 dataset. We test the models on a test set in which the background colors and fonts colors new colors unseen in V2. While testing, we feed the models with a pair of images from V2 and the test set that have the same attributes except for fonts colors and background colors. Then we compute the MSE loss between the images' latent spaces on the content, size and style part. The result shows that DEAE model has a lower average MSE loss of 71 ..2441 among the testing pairs while the GSL-AE model has loss of 682237.8125. The latent codes of content, size and style remain similar even the colors of fonts and background are unseen while training, which means our DEAE model has better ability for attribute disentanglement on unseen samples.
train both GSL-AE and DEAE the
3.3 PRECISE CONTROL. THE SYNTHESIS DIRECTION
Here explore the distribution of disentangled representation and mining the relationship between movement in high dimension x space and low dimension & space to answer two questions: (1) How can we change the attribute value towards a specific value (e-g., change its background color to red color)? (2) Which direction of movement can help us to find new attributes?
As for the first question, for each background color, we train binary color classifier label interpolated points in the ; space and assign a color score for each of them, then we use SVM to find the boundary and obtain UDV for this attribute value. Since the UDV the most effective direction to change the semantic score of samples, if we move 2 value of the given image towards UDV, its related semantic score would increase fast. As shown in Fig. (a), the 1st row represents the start image and the target image. In 2nd row, if we move the value step by step, the proportion of red in the background color will increase obviously. The 3rd row illustrates that for small intra-class variance attribute, we can directly move the z value to the cluster center of the target attribute, and this method guarantees that we can get the exact same background color.
As for exploring more new attributes, the combination of UDVs may be good choice. for example, the given picture is green, the new colors may fall in the path from green to blue as well as the path from green to red. Thus, it is reasonable to set move direction
Vblue + Ured Vgreen (v represents UDV). The 1s row of Fig. 7 (b) shows the results of changing z value with the combine vector Ubue + Ured Ugreen- On the contrast, the 2nd row only use Ubhte and the 3rd row only use Ured- We can find that both the 2nd and the 3rd row only find one color while the 1st row finds more.
3.4 DOWNSTREAM TASKS PERFORMANCE
We design a letter image classification experiment to explore the performance of DEAE as a generative model improve the classifier model performance based on the downstream training dataset.
We tailored three datasets from Fonts, each of them has ten letters as labels (Table. 1). The large training set (Dz) and testing set (Drest) have the same number of images with the same attribute values of font, size and font color ,and different background colors. We take subset of Dr, to form a small training set Ds with fewer attribute values. We first train the resnet18 model for classification tasks on D2 and Ds. calculating the test accuracy on the test dataset D Diest To evaluate the controllable synthesis performance of DEAE, we first train the model on Ds and then we use the trained DEAE to synthesize 1000 more images. We combine the synthesized images with Ds and form an augmented DS+DEAE training set. We also use the same training settings for GSL and form an augmented Ds+GSL-AR AE training set. We compare two synthesized datasets by training classifiers with the same settings and calculating the corresponding testing accuracy (Table. 1). From Ds to DS+DEAE. the test accuracy increases from 71% % 76%, which shows the addition of synthesized
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Figure 7: Changing to target attribute value and mining new attribute values
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images from DEAE trained model to the small training improve the performance of the classification task. The accuracy gap from DS+DEAE to Ds+GSL-AE shows that DEAE performs better than GSL-AE.
Table 1: Controllable augmentation performance (the * means that synthesized images with new attributes are added into training set)
Attributes
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Back- Color
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Test
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Accuracy
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3.5 DATASET BIAS ELIMINATION
Dataset bias may influence on the performance of model greatly. Mehrabi et al. (2019) listed lots of bias resources and proved that eliminate bias is significant. Our DEAE could be solution to biased problem. We design experiments to demonstrate how to achieve fairness with DEAE.
We design three datasets, biased training dataset D an unbiased training dataset DUB and an unbiased test dataset DT In DB we entangle the two attributes letter and background color as dataset bias. D consists of aaree-part: GI, G2, and G3. The details of those datasets can found in Table. and the number of colors represents the number of background colors for each letter.
Then, we use D and DUB to train letter classifier with resnet-18 respectively and test DJ, As is shown in Table. 2, the classifier trained on DB only gets 81 % test accuracy while classifier trained on DUB obtains 99% test accuracy. One possible explanation is that the entanglement of background colors and letters in the biased dataset results in the bad performance of the biased model. Grad-Cam (Selvaraju al. 2019)) is method that can highlight the region of pictures which plays decisive role in the classifying process. As shown in Fig. the results of Grad-Cam proved that the classifier would regard background color as essential information if it entangled with letters. We use the perfect disentangled latent space of DEAE to solve the entangled bias in D We first train a DEAE use DB. then we train the letter classifiers on the latent space instead of image space, where we explicitly drop the background color related dimensions and use the latent code input. After training, the accuracy of biased model in the test dataset is 98%. Hence, we eliminate the dataset bias with our DEAE model.
Table 2: Dataset setting and experiment results
Number of
Number of colors
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letters
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Figure 8: The influence of bias shown by Grad-Can
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4 CONCLUSION
We proposed new kind of generative autoencoder: Disentangled Exploration Autoencoder (DEAE), which can achieve controllable synthesis by freely interpolating in disentangled latent space. DEAE tries to turn the non-convex latent space to convex for each attribute by 'reusing' the encoder to regularize the latent space of synthesized images. We show that DEAE outperforms state-of-the-ar methods attribute controllable synthesis tasks. We demonstrate how DEAE achieves precise latent space movement and novel attribute mining in perfect disentangled space by combining unit direction vectors. We also demonstrate that DEAE, a generative model improve the performance of downstream classification tasks as well as eliminate dataset bias, which provides a new solution for fairness problems.
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Reviews:
[{"review_id": "x0SRbSVjht", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Summary: The paper proposes a new framework for adding a generative component to autoencoders without using adversarial or variational techniques. Further, the true motivation is to overcome issues due to naive interpolation (resulting in out of distribution samples) and to provide a semantic controllable synthesis in autoencoders. Empirical results show the proposed DEAE succeeds in both.\n\nStrengths:\n* I appreciate the intuition and examples throughout the paper, regarding the method itself and description of the experiments and desired outputs. This makes the paper easy to read and follow.\n* The provided results support the claims (although they lack exhaustiveness, see below).\n\n\nWeaknesses:\n* Although I agree with the intuition of the method, no theoretical analysis is provided to justify the proposed solution. \nThe empirical results seem to confirm the statements of contribution but in a very limited setup. A more exhaustive presentation would be more convincing.\n\nAlthough the paper builds on previous work, I find it quite interesting and significant to have a method for controllable synthesis. If the authors address my concerns, I am willing to increase my score.  \n\nQuestions:\n\nMethodology:\nThe main novelty in the proposed method is the L_{reg} loss term in the training of the DEAE, which was never explicitly written or explained beyond the intuitive interpretations. \n\n\nIs there any theoretical direction you can propose for justifying the claim that adding the regularisation loss helps to turn non-convex to convex latent spaces? If not, at least a toy experiment to validate this assumption might help.\n\nMissing reference:\nThe technique proposed for novel attribute mining, UVD is quite similar to the concept vectors proposed in TCAV [1].\n\nExperiments:\nOnly a few quantitative results were presented, and those are also missing standard deviations. Please add those to make the results complete.\n\nThe results for experiment 3.2, were the MSE losses normalized? If the latent spaces correspond to different models, how do we know if the numbers are comparable?\n\nOther baselines:\n- Generative models: at least some comparison to vanilla VAE, GANs as a sanity check (if the claim is generative AE)\n- Why is GSL-AE not used in 3.4?\n- Could you please provide more plots of the nice results (Figures 5 and 6) in the appendix just to confirm the presented results were not “cherry-picked”.\n\n\nMinor:\n- In Figure 5 the acronym for general AE is ‘AE’ instead of ‘GAE’\n- I don’t see the need for  ‘non-adversarial’ in the title.\n\n[1] Kim, Been, et al. \"Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav).\" International conference on machine learning. PMLR, 2018 ", "extra_scores": {}, "extra_text": {}}, {"review_id": "tq8HMJeqPs8", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Summary:\n\nThe paper presents an approach for controllable generation with auto-ecoders. It uses interpolation in a disentangled latent space using Group Supervised Learning. Generated samples are encoded back to the latent space as additional regularization to improve sample quality. The authors perform experiments on Fonts and RaFD datasets for controllable generation. They also show applications of their model in improving performance of classifiers and reducing bias.  \n  \n###########################################################\n\nStrengths: \n\nThe problem of controllable image generation is important and current approaches are far from perfect. Autoencoders with disentangled latent space are a promising candidate for this problem as they can both reconstruct the input and control the attributes. \n\n###########################################################\n\nWeaknesses:\n1. Literature review is inadequate and there are limited comparisons with related work. There are several other approaches that are not mentioned in the paper. Fader networks [A] also uses an encoder-decoder architecture trained to reconstruct images by disentangling salient information of the image and the values of attributes directly in the latent space. They provide a more comprehensive evaluation on CelebA and Flowers datasets while this paper use relatively simpler datasets, Fonts and RaFD. The authors need to clarify differences of their approach with [A] and provide comparisons with them. They need to include quantitative scores for their controllable generation. There are also other relevant papers such as Deep Feature Interpolation [B].\n2. Experiments are mostly performed on the Fonts dataset which is relatively simple. It would be better to perform experiments on datasets such as CelebA which has attribute annotations. \n3. Novelty of the proposed approach is limited as the authors use an existing method (GSL) for disentangling the latent space and also encoding the image back to latent space is proposed in other works such as InfoGAN and BicycleGAN.\n4. To obtain UDVs the authors train a binary classifier for each color value which is not efficient. \n5. It seems the bias experiment assumes that we know which attributes are entangled which might not be realistic. \n6. In figure 7(a) changes of colors are discrete while continuous changes are more desirable. \n7. In the results in Figure 2, it seems changing one attribute also affects the other.   \n\n############################################################\n\nReason for Rating:\n\nOverall, while autoencoders with disentangled latent space are a promising candidate for controllable generation, experimental results and novelty of the proposed approach are inadequate for publication. \n\n############################################################\n\nReferences: \n\n[A] Fader Networks: Manipulating Images by Sliding Attributes, Lample et al., NIPS 2017 \n\n[B] Deep Feature Interpolation for Image Content Changes, Upchurch et al., CVPR 2017\n\n#############################################################\n\nAfter author response: I thank the authors for their answers. However, as noted by other reviewers, experimental results and comparisons with related work are lacking, and I cannot increase my score without major changes to the paper. ", "extra_scores": {}, "extra_text": {}}, {"review_id": "s9mFCTSA7JW", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Summary\n\nThis paper proposed to explore a given disentangled latent code and explore it through random interpolations. The idea is to ensure these interpolated codes rely inside data convex hull by enforcing latent code reconstruction by first decoding and then reencoding this explored codes. The authors proposed a way to manipulate the final latent space by using Unit Direction Vector (UDV) to generate unseen attribute values. The results are illustrated on 2 datasets (Fonts and RaFD) both qualitatively and quantitatively.\n\nReason for score\n\nOverall I vote for reject, this paper has limited contribution and lack of consistency (disconnected ideas: DEAE and UDV), clarity (respective contributions of GSL-AE and DEAE) and justification (spurious and not convincing experimentations).\n\n\nPros\n1. The general topic of having more controlable synthesis by having more disentangled representation if of clear interest for the field. \n2. The idea of reconstructing random interpolations of valid latent codes by decoding/reencoding is interesting. I don't think this is novel, but not widspread enough in the field so far, so the interest remains. \n\nCons\n1. While the topic is of interest for the field, the interest of the proposed approach is clearly limited since it relies on a preprocessing step (GSL-AE) of which it is already the role. A clearer distinction of the contributions of each step would help to illustrate the additional interest of DEAE. Unfortunatly the paper relies too much on GSL-AE which limits the potential scope of their main contribution which could be wider.\n2. The title is misleading since it presents the approach to proposed a \"disentangled exploration\" while it is essentially provided by GSL-AE used as a preprocessing step. Thus, it would also be good to mention that the approach is supervised (like GSL-AE).\n3. The paper lacks of clarity: UDV or \"dataset bias elimination\" seems to be  spurious addition and are not really well connected in the paper. It's hard to understand how it contributes to illustrate the interest of DEAE.\n4. Results are not convincing.\n- MSE loss between latent spaces from different methods cannot be compared at all. We can imagine spurious differences only due to scale differences for instance.\n- Fig 2. is mentioned p.5 to illustrate the qualitative superiority of DEAE over GSL-AE: not really obvious of this state.\n- Section 3.3 + fig 7.: interpolation is not smooth which contradicts the quality of the representation and the interest of UDV.\n- Section 3.4: the idea of evaluating the performance of generative methods through their capacity of improving a classification task by augmenting the initial dataset, is very indirect and not really convincing.\n- Gradcam results (Fig. 8) does not illustrate any improvement in favor of background information for the \"unbiased model\"\n5. \"Dataset bias elimination\"\n- This section seems spurious. Nothing in the proposed approach is really specific to such an application appart the global context to which it is attached. I don't see what this section brings to the paper.\n- The way the unbias model is obtained is not realistic since you need to know what information has to be dropped, whereas in real applications you generally don't know the source of bias.\n6. While having thinner way of exploring the latent space is interesting, the proposed UDV approach sounds spurious and ad hoc and does not seem to be connected to DEAE.\n\nQuestions\n1. What is the context of application of your approaches since it relies on a supervised approach to create the initial disentangled embedding ? Coutrolable synthesis is generally understood in an unsupervised setting.\n2. Is there a link between UDV exploration and the way DEAE is learnt ?\n3. Why using \"random interpolation\" for inference and illustrating performance (in Fig. 5) ? It would be clearer with standard interpolation between left and right bounds. I also would expect that interpolations would reach (or at least get closer to) the targeted right bound.\n4. How do you ensure that MSE (from section 3.2) can be compared between totally different latent spaces ?\n5. Section 3.4: Why D_S is used rather than D_L for further analyses ? (to get augmented datasets)\n\n\nMinor comments\n1. Intro: Flows could be mentionned on top of VAE and GANs\n2. Intro on GAN & VAE limits are too much like a caricature regarding recent results. VAE can be used to generate HD images now, and GAN are much easier to train these days with GP strategy or different learning rates between generator and discriminator for instance. Approaches like InfoGAN could be mentioned to illustrate standard strategies to control GANs.\n3. Fig 2. Not clear which kind of interpolation has been used for GSL-AE and DEAE since neither font, font size, font color, letter nor background color are interpolated in the figures. This figure would be more powerful to stick to one (or a few) letter(s) and change only the considered attribute (background or font color). Here the comparison and the illustration of the effect is not easy.\n4. Section 2. \"whic h maps\"  \"which maps\"\n5. notation \"*\" has not been introduced (like in f_theta^* and g_phi^*)\n6. L_reg has not been introduced or referred in the paper.\n7. Fig 7. Choosing a fg color different from the targeted bg color would be better", "extra_scores": {}, "extra_text": {}}, {"review_id": "TmhImuIOn8", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "# Summary\nThis paper proposes a new method called Disentangled Exploration Auto-Encoder (DEAE). This new method is based on “(Ge at al.) Zero-shot synthesis with group-supervised learning” to which a modified cyclic loss term is added. This method is trained on datasets with label supervision.\n\n# Pros\n1. Compared to “(Ge at al.) Zero-shot synthesis with group-supervised learning” the results seem to be more visually pleasing.\n\n# Cons\n1. The method is not clearly presented. In particular the loss terms are not mathematically expressed as equations. I don’t know if we’re expected to read the paper this method is based on to have detailed equations. In any case, the added cyclic loss is expressed as an equation either, so it’s really hard to say what the method really does. It’s also unspecified how the latent space is allocated to attributes.\n2. The scope of the method is not clearly presented either: I was under the impression that the proposed method was comparable to other unsupervised auto-encoders while in fact it requires label supervision\n3. It seems the paper claims to be more than it actually is: if I understand correctly, the sole contribution of this paper is a cyclic loss term, compared the (Ge et al) paper which can be seen as a regularizer.\n4. Experiments are mostly focused on visual inspection of images and do not appear impressive to me (except for the toy dataset of colored letters, but then I don’t know what the SotA is like). Very few numerical results (save for dataset bias elimination) are presented. Results are not compared to other SotA methods, except the (Ge at al) paper which the method is based upon. Experimental setup is very incomplete.\n\n# Questions and nits\n1. It would have benefited my comprehension to mention in the abstract that the proposed method requires attribute/label supervision. In its current form, it seems to claim to solve disentanglement for unsupervised auto-encoders and I find it misleading.\n2. The mention of the generative ability without GAN based training in the abstract is also confusing: the proposed method seems orthogonal to GANs and could be combined with them.\n3. Several times I see the term “perfect disentanglement” to describe the improved disentanglement that this method offers compared to (Ge et al). What I don’t understand is what makes it “perfect”, can’t it be further improved?\n4. “<mention unsupervised auto-encoders>. We propose a different solution to empower precise attribute controllable synthesis ability on autoencoders: DEAE”. To be fair, it’s also a solution to a different problem scope. The proposed method uses attributes while the cited methods don’t. So it’s really a solution to a different problem altogether.\n5. Typo “whic h” => “which”\n6. “Fig. 4 (d) shows that we can combine the UDVs to dicover new attribute values.” Aside from the typo on \"discover\", it’s unclear how you discover new attributes values (which I assume are centroid like the example you mentioned before for the blue color). Here instead, my understanding is that UDV just provides a vector along which values of interest may lie. It seems the eventual decision to make a value an attribute is manually decided by a human after inspecting the effects along an UDV axe.\n7. Downstream task performance. It is a toy dataset and it’s hard to really tell the real power of the proposed method. A lot of information is missing, what are the sizes the $D_S$ and $D_L$? The only reported numbers are $D_S$ vs $D_{S+DEAE}$. What is the unreported accuracy gap between $D_{S+DEAE}$ and $D_{S+GSL-AE}$ that leads to the later conclusion that DEAE performs better? What is the accuracy for $D_L$? No information is known about the classifier network architecture(s), parameter sizes, tuning and whether or not they overfit or what other causes could be responsible for the observed results.\n\n=====POST-REBUTTAL COMMENTS======== \nI thank the authors for the response and the efforts in the updated draft. Some of my queries were clarified, particularly concerning missing experimental details. However, unfortunately, I still think more needs to be clarified in the actual paper write up, notably on the points of non-adversarial as well as the method description.", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: Overall, the paper makes some interesting and intuitive observations regarding the autoencoders with a cycle consistency, and aims at achieving controllable synthesis via a disentangled representation. However, the overall consensus was that the manuscript needs further iterations:

In particular:
The ideas should be made more precise using mathematical arguments, as it stands some ideas are (e.g. DEAE and UDV) disconnected.

The scope needs to be clarified, e.g. respective contributions of GSL-AE and DEAE, use of label information 

More numerical/quantitative evaluations, the current experimentation is not convincing enough, needed for better justification (spurious and not convincing experimentations)

The English of the manuscript could be improved as it occasionally hampers the flow.

Decision: Reject