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arxiv: 1907.09754 · v1 · pith:IRQRVVYJnew · submitted 2019-07-23 · 💻 cs.CV

Controlling biases and diversity in diverse image-to-image translation

Yaxing Wang , Abel Gonzalez-Garcia , Joost Van De Weijer , Luis Herranz This is my paper

Pith reviewed 2026-05-24 17:42 UTC · model grok-4.3

classification 💻 cs.CV
keywords image-to-image translationdiverse translationbiassemantic constraintsunpaired learningface translationobject translation
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The pith

Semantic constraints reduce unwanted biases in diverse image-to-image translation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper studies bias in diverse unpaired image-to-image translation, where models trained on skewed datasets introduce extra unwanted alterations such as shifts in gender or race when translating faces. It proposes semantic constraints that force the model to keep specific image properties fixed during the translation. These constraints are intended to cut the undesired changes while leaving the intended domain shift and the range of possible outputs intact. Tests on several heavily biased collections covering faces, objects and scenes indicate that the constraints achieve this separation of wanted and unwanted effects.

Core claim

By imposing semantic constraints that enforce the preservation of desired image properties, the model produces translations with fewer unwanted changes while still performing the wanted transformation, serving as a step towards unbiased diverse image-to-image translation.

What carries the argument

Semantic constraints that enforce preservation of desired image properties while sampling different style codes in a disentangled content-style latent space.

Load-bearing premise

That the observed biases arise primarily from the visual distribution of the target domain and that semantic constraints can be imposed without degrading translation quality or diversity.

What would settle it

A quantitative test on the face datasets in which the constrained model still produces measurable gender or race shifts at rates comparable to the unconstrained baseline would falsify the claim that the constraints successfully limit unwanted changes.

Figures

Figures reproduced from arXiv: 1907.09754 by Abel Gonzalez-Garcia, Joost Van De Weijer, Luis Herranz, Yaxing Wang.

Figure 1
Figure 1. Figure 1: Diverse image-to-image translation in a very biased set￾ting (domain A: mostly white males without makeup, domain B: white females with makeup): (a) biased translations, (b) with semantic constraint to alleviate bias while keeping rele￾vant diversity. possible to design better and more balanced datasets, or at least understand the related biases, their nature and try to incorporate tools to alleviate them … view at source ↗
Figure 2
Figure 2. Figure 2: Examples of training sets for image translation: (a) paired edge-photo, (b) unpaired young-old (well-aligned bi￾ases), and (c) unpaired without-with makeup (misaligned in gender). generative models, Mathieu et al. [30] combined a GAN with a Variational Autoencoder (VAE) to obtain an in￾ternal representation that is disentangled across specified (e.g. labels) and unspecified factors. InfoGAN [9] achieves so… view at source ↗
Figure 3
Figure 3. Figure 3: Geometric interpretation of the semantic constraint unbiasing [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Diverse image-to-image translation (DIT): (a) biased, (b) [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Robustness to bias on Biased makeup: (left) misclassification rate, (middle) drop in confidence, (right) ID distance. Input Direction MUNIT +PI DRIT UDIT UDIT+PI M Makeup 0.268 0.267 0.263 0.192 0.151 F Makeup 0.212 0.199 0.193 0.154 0.133 F Demakeup 0.297 0.293 0.253 0.208 0.203 [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example translations for Biased makeup when applying makeup to a male. UDIT uses identity as semantic constraint. than preserving gender, and implicitly also preserves it. For this reason, we use a semantic constraint based on identity (ID). We consider an off-the shelf network for face recognition [31] and select its highest level convolutional features as semantic feature. The model has been trained with… view at source ↗
Figure 7
Figure 7. Figure 7: Example translations on MORPH by biased DIT methods (MUNIT/DRIT) and our UDIT with semantic constraint on identity. ate over the gender that is underrepresented in the target domain. These results confirm the trends observed quali￾tatively in [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Robustness to bias on MORPH: (a)young to old and (b)old to young: (left) misclassification rate, (middle) drop in confidence, and (right) ID distance [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Robustness to bias in terms of misclassification rate and drop in confidence . In the case of the young female, gender is almost always changed due to the extreme bias towards males. UDIT, on the other hand, preserves the wanted semantic properties and outputs diversity without unwanted changes. Robustness to unwanted changes. Here we evaluate how the identity constraint impacts gender and ethnicity change… view at source ↗
Figure 7
Figure 7. Figure 7: 6.6. Cityscapes → Synthia-night Semantic constraint. We train a binary classifier for daytime classification based on VGG16 [38] using both real and synthetic images. We use 6000 realistic images from BDD-100K [44] with a 50/50 daytime distribution. As synthetic images we use 6000 images from a disjoint sub￾set of Synthia [36], also with a balanced class distribution. We consider two semantic constraints. … view at source ↗
Figure 10
Figure 10. Figure 10: Results on Cityscapes → Synthia-night. Example translations by MUNIT and UDIT with two variants of the semantic constraint. increases the required dimensionality on the semantic fea￾tures. Results [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Robustness to bias on Biased handbags [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Example translations for Handbags-texture (left) and Handbags-color (right). Better viewed electronically, zoom might be necessary to appreciate the changes in texture. [5] Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D., 2017. Unsupervised pixel-level domain adaptation with gen￾erative adversarial networks, in: Proceedings of the IEEE Con￾ference on Computer Vision and Pattern Recognitio… view at source ↗
read the original abstract

The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT), an even more challenging setting in which an image can have multiple plausible translations. This is normally achieved by explicitly disentangling content and style in the latent representation and sampling different styles codes while maintaining the image content. Despite the success of current DIT models, they are prone to suffer from bias. In this paper, we study the problem of bias in image-to-image translation. Biased datasets may add undesired changes (e.g. change gender or race in face images) to the output translations as a consequence of the particular underlying visual distribution in the target domain. In order to alleviate the effects of this problem we propose the use of semantic constraints that enforce the preservation of desired image properties. Our proposed model is a step towards unbiased diverse image-to-image translation (UDIT), and results in less unwanted changes in the translated images while still performing the wanted transformation. Experiments on several heavily biased datasets show the effectiveness of the proposed techniques in different domains such as faces, objects, and scenes.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper addresses the problem of bias in diverse unpaired image-to-image translation (DIT), where target-domain statistics can induce unwanted attribute changes (e.g., gender or race in face images). It proposes adding semantic constraints to existing disentanglement-based DIT models to enforce preservation of desired properties, yielding a model for unbiased diverse image-to-image translation (UDIT) that reduces such changes while retaining the intended translation and output diversity. The abstract states that experiments on multiple heavily biased datasets across faces, objects, and scenes demonstrate the effectiveness of the approach.

Significance. If the semantic-constraint mechanism can be shown to measurably reduce unwanted attribute shifts without loss of translation fidelity or diversity, the work would provide a practical extension of disentanglement techniques that directly targets fairness issues in generative vision models. The absence of any reported metrics, however, prevents evaluation of whether the central claim holds.

major comments (1)
  1. [Abstract] Abstract: the claim that 'experiments on several heavily biased datasets show the effectiveness of the proposed techniques' supplies no quantitative results, ablation studies, error analysis, or even example metrics (e.g., attribute classification accuracy before/after, diversity scores such as LPIPS, or FID). Without such evidence the data-to-claim link cannot be assessed and the central assertion that unwanted changes are reduced while wanted transformations and diversity are preserved remains unevaluated.
minor comments (1)
  1. [Abstract] Abstract: the acronym 'UDIT' is introduced before its expansion ('unbiased diverse image-to-image translation') is given, which is a minor clarity issue.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed review and constructive feedback. We address the major comment below and will revise the manuscript accordingly to strengthen the presentation of our results.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'experiments on several heavily biased datasets show the effectiveness of the proposed techniques' supplies no quantitative results, ablation studies, error analysis, or even example metrics (e.g., attribute classification accuracy before/after, diversity scores such as LPIPS, or FID). Without such evidence the data-to-claim link cannot be assessed and the central assertion that unwanted changes are reduced while wanted transformations and diversity are preserved remains unevaluated.

    Authors: We agree that the abstract would be strengthened by including key quantitative metrics. The full manuscript reports attribute classification accuracies (to quantify reduction in unwanted bias-induced changes), LPIPS scores (for diversity), and FID (for translation quality) in the experiments section across the evaluated datasets. We will revise the abstract to explicitly cite representative results (e.g., X% reduction in attribute shift with no loss in LPIPS or FID) while preserving the overall length and readability. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper proposes extending existing diverse I2I translation frameworks (e.g., via disentanglement of content/style) with an added semantic constraint term to reduce unwanted attribute shifts induced by target-domain statistics. The central claim rests on experimental validation across biased datasets rather than any closed-form derivation, fitted parameter renamed as prediction, or self-citation chain. No equations, uniqueness theorems, or ansatzes are described that reduce the method to its inputs by construction; the approach is presented as an empirical extension with external constraints, consistent with the reader's assessment of score 1.0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are identifiable from the provided text. The approach implicitly relies on standard assumptions of disentangled latent representations in GAN-based translation models.

pith-pipeline@v0.9.0 · 5736 in / 1095 out tokens · 33737 ms · 2026-05-24T17:42:34.827855+00:00 · methodology

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