Diffusing DeBias: Synthetic Bias Amplification for Model Debiasing
Authors: Massimiliano Ciranni
Vito Paolo Pastore
Roberto Di Via
Enzo Tartaglione
Francesca Odone
Vittorio Murino
Venue: NeurIPS 2025 poster ()
Keywords: Model debiasing
bias amplification
diffusion models
image classification
Primary area: applications

Abstract: The effectiveness of deep learning models in classification tasks is often challenged by the quality and quantity of training data whenever they are affected by strong spurious correlations between specific attributes and target labels. This results in a form of bias affecting training data, which typically leads to unrecoverable weak generalization in prediction. This paper addresses this problem by leveraging bias amplification with generated synthetic data only: we introduce Diffusing DeBias (DDB), a novel approach acting as a plug-in for common methods of unsupervised model debiasing, exploiting the inherent bias-learning tendency of diffusion models in data generation. Specifically, our approach adopts conditional diffusion models to generate synthetic bias-aligned images, which fully replace the original training set for learning an effective bias amplifier model to be subsequently incorporated into an end-to-end and a two-step unsupervised debiasing approach. By tackling the fundamental issue of bias-conflicting training samples’ memorization in learning auxiliary models, typical of this type of technique, our proposed method outperforms the current state-of-the-art in multiple benchmark datasets, demonstrating its potential as a versatile and effective tool for tackling bias in deep learning models. Code is available at https://github.com/Malga-Vision/DiffusingDeBias

Diffusing DeBias: Synthetic Bias Amplification for Model Debiasing
Massimiliano Ciranni xM Vito Paolo Pastore 1.2- Roberto Di Via
Enzo Tartaglione
Francesca Odone
Vittorio Murino
MaaLGa-DIBRIS, University of Genoa, Italy
AI for Good (AIGO), Istituto Italiano di Tecnologia. Genova, Italy Teelécom- Ecole Polytechnique Superior, France Department of Computer Science, University of Verona, Italy
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coberto.divia)eee.. dediaa unige it, vito. vito.paolo paolo, pastore@unige, it
Abstract
The effectiveness of deep learning models in classification tasks is often challenged by the quality and quantity of training data whenever they are affected by strong spu- rious correlations between specific attributes and target labels. This results in form of bias affecting training data, which typically leads to unrecoverable weak gener- alization in prediction. This paper addresses this problem by leveraging bias ampli- fication with generated synthetic data only: we introduce Diffusing DeBias (DDB), novel approach acting plug-in for common methods of unsupervised model debiasing, exploiting the inherent bias-learning tendency of diffusion models data generation. Specifically, our approach adopts conditional diffusion models generate synthetic bias-aligned images, which fully replace the original training set for learning an effective bias amplifier model be subsequently incorporated into end-to-end and a two-step unsupervised debiasing approach. By tackling the fun- damental issue of bias-cone chas-confii training samples' memorization in learning auxil- iary models, typical of this type of technique, our proposed method outperforms the current xaateoof-the-ar in multiple benehmark datasets, demonstrating its potential as versatile and effective tool for tackling bias in deep learning models. Code is available wtttt:t://// htt.//..ttt....../ com/ ga- Wision/Diffuui /iission/DifffsinnneeBBsss
1
Introduction
Deep learning models have shown impressive results various vision tasks, including image clas- sification. However, their success is highly dependent on the quality and representativeness of the training data. When deep neural network trained on biased dataset, so-called "shorteuts", corre- sponding spurious correlations (i.e., biases) between irrelevant attributes and labels, are learned instead of semantic class attributes, affecting model generalization and impacting test performances. In other words, the model becomes biased towards specific training cobppopulations presenting biases, known as bias-aligned, while samples not affected by the bias are referred to as bias-conflicting or unbiased. In this scenario, where it is generally assumed that most of the training samples available in biased data set are biased (typically, 95% or higher (39]), several debiasing methods have been
proposed addressing the problem from different points of view 23 46 22] 29 When annotations
on bias attributes are available, common supervised debiasing strategies involve reweighting the training to give more importance the few available bias-conflicting data 22) for example, by upsampling 31 or upweighting 43 them. Conversely, unsupervised debiasing methods, which
"These authors contributed equally to this work.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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assume no prior availability of bias information, typically involve an auxiliary model to estimate and convey information on bias for reweighting the training set, employing its signal to guide the training of debiased target model, while diminishing its tendency to learn bias shortcuts.
To this end, there are several existing works [39 23 29 44 exploiting intentionally biased auxiliary models, trained to overfit bias-aligned samples, thus being very confident in both correctly predicting bias-aligned training samples as well as misclassifying bias-conflicting ones (29). Such a distinct behavior between the two subpopulations is required to convey effective bias supervisory signals for target model debiasing, including gradients or per-sample loss values, which, e.g., should ideally be as high as possible for bias-conflicting and low as possible for bias -aiinne [39]. However, such desired behavior is severely hindered by bias-conflicting training samples, despite their low number. Indeed, the low cardinality of this population impedes a suitable modeling of the correct data distribution, yet, they are sufficient to harm the estimation of a reliable bias-aligned data distribution, despite the latter having much higher cardinality. This scenario occurs under two main conditions. First, bins-conflicting samples in the training set act as noisy labels, affecting the bias learning for the auxiliary models [29]. Second, these models tend to overfit and memorize bias-conflicting training samples within very few epochs, failing to provide different learning signals for the two subpopulations [9]. Despite the available strategies, including the usage of the Generalized Cross Entropy (GCE) loss function [39 41, ensembles of auxiliary models [23] 29] or even ad hoc so- lutions tailored to the specific dataset considered (e.g., training for only one epoch or assuming the availability of bias-annotated validation [35)), how to define a protocol for effective and robust auxiliary model training for unsupervised debiasing remains an open issue.
Remarkably, recent works [41 [2 23 underline how the target model generalization is profoundly impacted by the protocol used to train such an auxiliary model. In principle, if one could remove all bias-confie confcconfi samples from the training set, it would be possible to train a bias-capturing model robust to bias-confiie confiiicfi samples' memorization and interference, and capable of conveying ideal learning signals (29].
To this aim, our intuition is to elude these drawbacks by skipping the use of the actual training data in favor of another set of samples synthetically generated by an adequately trained diffusion model. Specifically, we propose to circumvent biascconfiicting interference on auxiliary models leveraging synthetic biased data obtained by exploiting Conditional Diffusion Probabilistic Models (CDPMs). We aim to learn a per-class biased image distribution, from which we sample an amplified synthetic bias-aligned subpopulation. The resulting synthetic bias-aligned samples are then exploited for training a Bias Amplifier model, instead of using the original actual training set, solving the issue of bias-conflicting overfitting and interference by construction, as the bias-capturing model does not see the original training set at all. We refer to the resulting approach as Diffusing DeBias (DDB). The intuition is indirectly supported by recent works studying the problem of biased datasets for image generation tasks [8] 24), and showing how diffusion models' generations can be biased, as a consequence of bias present in the training sets (See Sec. 2
This tendency can be seen as an undesirable behavior of generative models, which affects the fairness of generated images, and recent articles have started to propose solutions decrease the inclination of diffusion models to learn biases [21] 24 42). However, we claim that what is considered a drawback for generative tasks can be turned into an advantage for image classification's unsupervised model debiasing, as it allows the training of a robust auxiliary model that can ideally be plugged into various debiasing schemes, solving bias-conflicting training samples memorization by construction. For this reason, we refer to DDB its effectiveness and versatility, we incorporate DDB's 's bias amplifier into two different debiasing frameworks, which we refer to as Recipe / and Recipe II, including a two-step and an end-to-end approach (see Sec. 2) Specifically, we exploit our bias-amplifier to: (i) extract subpopulations further used as pseudo-labels within the popular G-DRO [43) algorithm (Recipe I, Sec. (3..1), and (ii) provide a loss function for the training set to be used within an end-to-end method (Recipe II, Sec. 3.3.2) Both approaches are competitive with the state-of-ther the popular benchmark datasets. In summary, our main contributions can be regarded as follows:
plug-in for debiasing methods in image classification. To prove
We introduce DDB, a novel and effective debiasing framework that exploits CDPMs to learn per-class bias-aligned distributions in biased datasets with unknown bias information. The resulting CDPM is used to generate synthetic bias-aligned images later used to train a robust bias amplifier model, thus avoiding real bias-conflcting training sample memorization and construction interference (Sec. 3)
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We propose the usage of DDB as a versatile plug-in in for debiasing approaches, designing two recipes: one based on a two-step procedure (Sec. B.3.I]. and the other as an end-to-end (Sec. 3.3.2) algorithm. Both proposed approaches prove to be effective, achieving of the-art results (Sec. 3 across popular benchmark datasets, characterized by different types of single or multiple biases.
2 Related Work
In this section, we provide a broad categorization of model debiasing works.
Supervised Debiasing. Supervised methods exploit available prior knowledge about the bias (e.g.. bias attribute annotations), indicating whether a sample cxhibits particular bias (or not). A common strategy consists of training a bias classifer to predict such attributes so that it can guide the targer model to extract unbiased representations [ 48). Alternatively, bias labels can be thought of as pre-defined encoded subgroups among target classes, allowing for the exploitation of robust training techniques such as G-DRO [33. At the same time, bias information can be employed to apply regularization schemes aiming at forcing invariance over bias features
Unsupervised Debiasing. It considers bias information unavailable (the most likely scenario in real-world applications), thus having the highest potential impact among the described paradigms. Among such methods, popular strategy consists in involving auxiliary models at any level of the debiasing process. We categorize these methods into two-step B5 22]. and end-to-end 39 33 in the following paragraphs.
End-to-end approaches. Here, bias mitigation performed online with dedicated optimization objectives. Employing techniques such as ensembling and noise-robust losses has been proposed, though they require careful tuning of all the hyperparameters, which may require the availability of validation sets with bias annotations to work. In Learning from Failure (LfF), Nam et al. [39 assume that bias features are learned earlier than task-related ones: they employ a bias-capturing model to focus on easier (bias-aligned) samples through the GCE loss (33. At the same time, debiased network is jointly trained to assign larger weights to samples that the bias-capturing model struggles to discriminate. In DebiAN, Li et al. (33) jointly train an auxiliary model (the discoverer) and classifier (the target model) in an alternate scheme so that the discoverer can identify bias learned by the classifier during training, optimizing ad-hoc loss function for bias mitigation.
Two-step methods. Here, the auxiliary model is employed in a first step to identify bias in training data, generally through pseudo-labeling, while the second stage consists of actual bias mitigation exploiting the inferred pseudo-labels. In (35), the auxiliary model is trained with standard ERM and then used in inference on the training set, considering misclassified samples as bias-conflicting and vire-versa: debiasing brought on by up-sampling the predicted bias bias-confficting samples. Notably, they must rely on validation set with bias annotations to stop the auxiliary model's training after a few epochs to avoid memorization and to tune the most effective upsampling factor. In [23], a set of auxiliary classifiers is ensembled (bootstrap ensembling) into a bias- commmitteeeeeee,, proposing that debiasing can be performed using a weighted ERM, where the weights are proportional to the number of models in the ensemble misclassi- fying a certain sample. Finally, a recent trend involves the usage of vision-language models to identify bias, further exploiting bias predictions to mitigate biases in classification models [10 25]
Among the described methods, some exploit the auxiliary model for bias amplification (29 39 44
under the general assumption that a bias amplification model would either misclassify or be less confident on bias-conflicingg samples. In this case, the final debiasing performance is heavily dependent on such behavior, and specifically, on how strongly the auxiliary model captures the bias in training data [1 [29]. However, this fundamental assumption only holds if biss-confiitting samples are not memorized during training, which is likely to happen in very few training epochs [11 especially considering that the very few training coaffcnnfii samples are more likely to be overfitted, as reported in 43 5.. One could argue that an annotated validation set could be used for properly regularizing the bias amplification model, but the latter cannot be assumed available a priori in real cases, being this an emerging argument of discussion among the community 9 41 For this reason, differently from the previously cited research, we propose in this work that synthetic bias-aligned samples could be used instead of the original training set, solving the bias-conflictnng
3

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memorization issue by construction. However, real-world scenario using data with unknown bias poses a problem for generating a pure distribution of bias-aligned samples. As a solution, we propose to leverage diffusion models for generating a synthetic bias-aligned training set to be used for training a bias amplifier.
While the vast majority of available works regarding generative models and debiasing refer to strategies for obtaining unbiased synthetic data generation [T to the best of our knowledge, we are the first to propose the use of generative models for obtaining amplified biased distribution. The closest work employing generative models for model debiasing is Jung et al. [19], where a debiasing method exploiting GAN [2] is introduced, which is trained for style-transfer between several biased appearances with different objectives. Here, a target model is debiased through contrastive learning between images affected by different biases for bias itas-invrriant representations. Similar strategies, where bias-conflicting samples are augmented to support model debiasing, can be found in a few other works [34 18 28 In 28]. the problem of bias-conflicting memorization is stressed, proposing a bias-unsupervised strategy to augment and increase the diversity of bias-conflicting features, exploiting bias amplifier analogously to [39 for their identification, while improving the generalization of target debiased model. In (34], an intentionally biased auxiliary model is exploited, which is trained on the biased dataset, supplementing bias-conflicting samples for the target debiased model. Here, images are generated by solving an optimization problem consisting of attacking the auxiliary model predictions while preserving the ones of the target debiased model. In [18. bias-confiicinng samples are again augmented exploiting the popular mi approach [50]. Specifically, an auxiliary model is employed to amplify casy-to-learn features, and mix-up pairs with either the same ground truth label, but different biased features (evaluated with clustering in the feature space of an intentionally biased model), or samples with different biased features (i.e., belonging to different clusters), but showing the same ground truth label. Other works have also explored generative models for debiasing. For instance, BiaSwap (22 utilizes image translation techniques to swap bias attributes between images, aiming to create more balanced training data. However, methods that attempt to create synthetic bias-conflcting samples [22 34] may require high semantic fidelity, while our method creates a functionally biased dataset, which we believe to be simpler and more robust objective.
Differently from the previously described approaches, are proposing alternative strategy to avoid bias-conflicting memorization, while significantly improving the generalization of the target debiased model. In our work, we show how it is possible to leverage the intrinsic tendency of diffusion models to capture bias in data, using conditioned generations to infer the biased distribution of each target class, thus allowing the sampling of new synthetic images, sharing the same bias pattern of the original training data. Our method designs a memorization- bias amplified model, capable of providing precise supervisory signals for accurate model debiasing, virtually compatible with any debiasing approach involving auxiliary models.
3 The Approach
Our proposed debiasing approach is schematically depicted in Figure 1 In this Section, we first describe the general problem setting (Sec. (31),, then detail DDB's two main components: bias diffusion (Sec. 3.2h and the two Recipes for model debiasing (Sec. 3.3
3.1 Problem Setting
Let us consider general data distribution Piam. typically encompassing multiple factors of variation and classes, and to build dataset of images with the associated labels D = [(x,, W:) ) sampled from such distribution. Let us also assume that the sampling process to obtain D is not uniform across latent factors of variations, i.e., possible biases such as context, appearance, acquisition noise, viewpoint, etc.. In this case, data will not faithfully capture the true data distribution (praw) just because of these bias factors. This phenomenon deeply affects the generalization capabilities of deep neural networks classifying unseen examples not present the same biases. In the same way, we could consider 'D as the union between two sets, i.e.D = D Dunbiused D Here, the elements of Dunhiased are uniformly sampled from Pdata and, in D Drinsd, they are instead sampled from a conditional distribution Pduta (x. b). with b E B being some latent factor (bias attribute) from set of possible attributes B, likely to be unknown or merely not annotated, in a realistic setting [24]. [Drussed > [Puniissaal optimizing classification model fe over D likely results in biased predictions

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and poor generalization. This is due to the strong correlation between b and often called spurions correlation, and denoted as p(y, b), or just p for brevity 12 43 38) true target distribution semantics.
which dominating over the
It is important notice that data bias is general problem, only affecting classification tasks but also impacting several others such as data generation [8] For instance, given a Conditional Diffusion Probabilistic Models (CDPM) modeled as neural network Go [x y) (with parameters Φ) that learns to approximate a conditional distribution (x y) from D. we expect that its generations will be biased, as also stated in 24 While this is a strong downside for image-generation purposes, in this work, we claim that when p(y, b) is very high (e.g.2 0.95, as generally assumed in model debiasing literature [39), a CDPM predominantly learns the biased distribution of specific class, i.e. 3s (x v) = p [x| b) rather than (x v).
3.2 Diffusing the Bias
In the context of mitigating bias in classification models, the tendency of a CDPM to approximate the per-class biased distribution represents key feature for training an auxiliary bias amplified model.
The Diffusion Process. The diffusion process progressively converts data into noise through a fixed Markov chain of T steps [1]. Given data point Xo, the forward process adds Gaussian noise according to a variance schedule [ resulting in noisy samples X1..... Xy. This forward process can be formulated for any timestep t as: q(x, xo) N((x; vāzxo, (1 āi I)), where ā; II.m1 a, with a, = B.. The reverse process then gradually denoises a sample, reparameterizing each step to predict the noise using a model EØ:
1 var
B. VI
co (xxx,)) + Oi2,
x
X1
az
where z I), and o. VB..
Classifier-Free Guidance for Biased Image Generation. In cases where additional context or conditioning is available, such as class label y. diffusion models can use this information to guide the reverse process, generating samples that better reflect the target attributes and semantics. Classifier Guidance (CFG) [66] introduces flexible conditioning approach, allowing the model to balance conditional and unconditional outputs without dedicated classifiers.
The CFG technique randomly omits conditioning during training (e.g., with probability Puncond = 0.1), enabling the model to learn both generation modalities. During the sampling process, guidance scale w modulates the influence of conditioning. When = O, the model relies solely on the conditional model. As w increases (w 2 1), the conditioning effect is intensified, potentially resulting in more distinct features linked to y,, thereby increasing fidelity to the class while possibly reducing diversity, whereas lower values help to preserve diversity by decreasing the influence of conditioning. The guided noise prediction given by:
6, = (1 (Xe,, v) wen (),
2
where eg (xt, 4, is the noise prediction conditioned on class label y. and 6x(x,, () is the uncondi- tional noise prediction. This modified noise prediction replaces the standard eø (x L) term in the reverse process formula (Equation D In this work, empirically show how CDPM learns and amplifies the underlying biased distribution when trained on a biased dataset with strong spurious correlations, allowing bias-aligned image generation.
3.3 DDB: Bias Amplifier and Model Debiasing
As stated in Sec. a typical unsupervised approach to model debiasing relies on an auxiliary intentionally- model, named here as Bias Amplifier (BA). This model can be exploited in either 2-step or end-to-end approaches, denoted here as Recipe I and Recipe I1, respectively. This section describes the two debiasing recipes we employ in this work to demonstrate the effectiveness of our BA as plug-in for unsupervised model debiasing.
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A) Diffusing the Bias
B) Blas Ampliñier + Model Debiasing
Clasiher-Fres lrftusionn Guidance
The--lage
Bactga End
Vedel
Deleaingg
Figure I: Schematic representation of our DDB framework. The debiasing process consists of two key steps: (A) Diffussig the Bias uses conditional diffusion model with classifier-free guidance to generate synthetic images that preserve training dataset biases, and (B) employs Bias Amplifier firstly trained such synthetic data, and subsequently used during inference to extract supervisory bias signals from real images. These signals are used to guide the training process of a target debiased model by designing two debiasing recipes (i.e,, 2-step and end-to-end methods).
Real Images
i Blas Amplifer
Blas Pseudo-Labeling
Real Images
# Ran Amplier
Weighting Fector
Podie
Deblasing
Cdebiosing
Debiased Model
Group-DRO
Model
Overview of Recipe II's end-to-end debiasing ap- proach.
(a) Overview Recipe I's 2-step dehiasing approach.
Figure 2: Comparison of two debiasing strategies: Recipe and Recipe II.
3.3.1 Recipe I: 2-step debiasing
The adopted 2-step approach consists of 1) applying the auxiliary model trained biased generated data to perform a bias pscudo-labeling, hence estimating bias- suas-alignedbbia--comm actual data, and 2) applying bias supervised method to train debiased target model for classification. For the latter, we use the group DRO algorithm [43 (G-DRO) as a proven technique for the pure debiasing step. In other words, being in the unsupervised bias scenario where the real bias labels unknown, we estimate bias pseudo-labels performing an inference step by feeding the trained BA with the original actual training data, and identifying bias-aligned the correctly classified samples, and as bias-conflicting those misclassified. Among possible strategies to assign bias pseudo-labels, such feature-clustering <4] anomaly detection 41. adopt simple heuristic based the BA misclassifications. Specifically, given a sample (x,, c)) with unknown pseudo-label indicating whether x, is bias conflicting or aligned, we estimate bias-conflicting samples
split of original
1= (g: # y A C(D.. U.) > Pn (C( (C),
(3)
where is the indicator function, C is the CE loss of the BA on the real sample, and μ., and represent the average training loss and standard deviation, respectively, depending on the loss Together with the multiplier E N. this condition defines sort of filter over misclassified samples, considering them as conflicting only if their loss is also higher than the mean loss increased by a quan- tity corresponding to certain z-score of the per-sample training loss distribution ( [n,, (C) + (C) in Eq. 3). Once bias pseudo-labels over original training data are obtained, we plug in our estimate as group information for the G-DRO optimization, schematically depicted in Figure 2a
The above filtering operation refines the plain error set, restricting bias- confficconiingggg sample selection to the hardest training samples, with potential benefits for the most difficult correlation settings (p 0.99). Later in the experimental section, we provide an ablation study comparing different filtering (7) configurations and plain error set alternatives.
3.3.2 Recipe II: end-to-end debiasing
typical end-to-end debiasing setting includes the joint training the target debiasing model and one [39 or more 23 29] auxiliary intentionalll-biase models. Here, design end-to-end

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Waterbirds
BFFHQ
BAR
ImageNet-9
Urban Cars
Figure 3: Examples of synthetic bias-aligned images across multiple datasets. Each grid shows synthetic images for specific classes across biased datasets, revealing how the model aligns with dataset-specific biases in different contexts.
debiasing procedure, denoted Recipe II, incorporating our BA by customizing widespread general scheme, introduced the Learning from Failure (LIF) method [39]. LiF leverages an intentionally-bbasee model trained using Generalized CE (GCE) loss to support the simultaneous training of a debiased model adopting the CE loss re- re-weighted by a per-sample relative difficulty score. Specifically, replace the GCE biased model with our Bias Amplifier, which is frozen and only employed in inference to compute its loss function for each original training sample (Ceias amp), schematically represented in Figure 2b Such a loss function is used to obtain weighting factor LBis Aup
for the target model loss function, defined as Caciniu + Aup
Coarsely speaking, r should be
low for bias-aligned and high for bias-conficting samples.
Experiments
In this Section, we report the description of the benchmark datasets employed, followed by discussion of the obtained results and thorough ablation analysis. In the Appendix, we report the general implementation (Sec. [A.)] and training details, including the implementation details of Recipe I (Sec. A.3) and Recipe II (Sec. A.4), more examples and analysis the generated images (Sec. A.6 and additional debiasing results on less biased settings for Waterbirds plus an alternative version of BAR having known p and bins-anno.ations.
4.1 Datasets
Our experiments utilize six distinct datasets: three typical benchmark datasets, including Water- birds [43,, Biased Flickr-Faces-HQ (BFFHQ) 22 and Biased Action Recognition (BAR), taken from the original version as introduced in 33 On top of these benchmark datasets, we are inter- ested in evaluating
approach in challenging scenarios involving real-world images with natural biases and datasets where multiple shorteuts are present. For this reason, we include ImageNet- 9 [e//maa [14), and UrbanCars
(32 in our experiments. More dataset details are in Sec. A.
4.2 Results
4.2.1
Synthetic bias-aligned image generation
First, we present qualitative image generation results across multiple datasets (see Fig. validate the effectiveness of our bias diffusion approach. The generated samples maintain good fidelity and capture the typical bias patterns of each training set per class, confirming the conditioned diffusion process's 's ability to learn and diffuse inherent biases. This is confirmed by screening the generated images by three independent annotators, who were asked to verify (or deny) the presence of the bias in each synthetic sample, where possible. Specifically, for Waterbirds, the reported average bias-aligned proportion of synthetic generated images is 98.35%, BAR 99. 20%, and 99.70% is obtained for BFFHQ. All these values are higher than the known proportions of bias in the original datasets. addition, focusing on Waterbirds, we also evaluated the generated image content by adopting image captioner, specifically BLIP-2 [30]. We estimate caption for each image per

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class and count the frequency of every word. Supplementary Material (Sec. A.8) shows a histogram of the detected keywords, which it can be noted that together with high frequency of the object class (bird), similar significant bins of the bias attributes (land, water) are also evidenced.
4.2.2 Classification accuracy
and 3 report the average and standard deviation our employed metries, obtained over three independent runs for all the considered benchmark datasets. We dedicate separate table for each dataset, as not all methods share the same benchmarks. We report Worst Group Accuracy (WGA) for Waterbirds, as in 43 29) For BFFHQ. we report the average and the conflicting accuracy (i.e., the average accuracy computed only on the bias clas-conflicting test samples), following 22 B3 BAR and ImageNet-A do not provide bias-attribute annotations, hence, report just the average test aceuracy. For simplicity, throughout this section, we will refer to Recipe and Recipe I/ as DDB-I and DDB-II, respectively. It is worth noticing that, for BAR and BFFHQ. report benchmark results related to works that employ the same versions as ours. Across all the considered
Tables La
lb
2a
2b
Table I: Results of DDB Recipes and II on Waterbirds (left) and BAR (right), Ticks (V) mark bias-unsuu unsupervised methods. Best unsupervised results are in bold; second-bes are underlined.
(a) Debiasing results Waterbirds
Unsup Waterbirds
Method
(b) Debiasing results BAR
WGA
ERM
62.60 0.30
Method
Unsup
BAR
LISA
49
89.20 91,40 1.10
Avg.
G-DRO
ERM
51.85= 5.92
George
4F
76, 26.20=22 2.00
JTT 35
83.80= 1.20
BiaSwap
23
52.40
CNC
88, 50 0.30
JTT
68.53= 3.29
B2T+G-DRO 25
90.70= 0.30
LIF 39
62.98 2.76
39
78.
LWBC 23
62.03= 2.76
CDvG+LiF 19
84.80
DebiAN 33
69.88 2.92
MoDAD 41
80 =0:43:0 1.69
MoDAD
69.83= 0.72
DDB-II (ours)
01.56: 0.15
DDB-II (ours)
72.81 1.02
DDB-I (ours)
/
90.81 0.68
DDB-I (ours)
70.40= 1.41
DDB-I (w/ err. set)
90.34 0.41
DDB-I (w/ err. set)
70.59 0.19
benchmark datasets, both DDB recipes show state-of- ofte--of-the-art results. Notably, in Waterbirds, DDB's s WGA is higher than all the other state-of-the- thethe-rrt unsupervised methods' accuracies, well of that of the supervised G-DRO 43 (91.56% vs. 91.40% of G-DRO). DDB-I is providing slightly worse performance than DDB-II, but still better than the state of the art. In BAR, DDB-II is reaching
Table 2: Results DDB Recipes and II on BFFHQ (left) and ImageNet-9/A (right) from [2 Ticks (v) mark bias-unsupervised methods. Best unsupervised results are in bold; second-best are underlined.
(a) Debiasing results BFFHQ
Method
Unsup
BFFHQ
Avg.
(b) Debiasing results on ImageNet-9/A
ERM
60.13 0.46
BiaSwan
221
58, 87
Method
Unsup
ImageNet-A
KV]
62.97 3.22
Avg.
ETF Debias
47
75.60 1.22
ERM
30.30
Park et al.
400
71.68
CDvG+Lie
19
02.20.1045
LWBC CDvG+ FF
A
/
35.97=20.0
DebiAN 33
62.801
19
34.60
MoDAD
DDB-II (ours)
83. 13. 1.66
70.93: 0.14
DDB-II (ours) DDB-I (ours)
/
37.53-+082
DDB-I (ours)
81.27= 0.88
74.67 2,37
DDB-I s00)
82. 41 0.64
71.40:
DDB-1 (w/ cm: set)
/
38. 1240996
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72.81% average accuracy, which is 2.93% better than the second best (DebiAN (33 in terms of conflicting accuracy. In BFFHQ, our DDB-I reaches an accuracy of 74.67%, with an average improvement of I 1 when compared to the state-of-the-art ETF-DDeias 47 (73.60%). For UrbanCars
Table 3: Comparison of deep learning methods on the UrbanCars biased dataset. ID,ACC is in- distribution accuracy (the higher, the better); other three metrics show the accuracy gap between each minority group and ID,ACC (the lower, the better). GroupDRO and LLE are separated as they rely on bias information.
Method
IDACCC
BG-Gap | CoObj-Gap | BG-CoObj-Gap.
GrounDRO
43
91.60
10.90
3.60
16.40
96.70
2.10
2.70
LfF
[9]
97.2 20
11.60
18.40
63.20
JTT R61
95.90
8.10
13.30
40.10
EIIL F1
95.50
4.20
24.70
11.90
DebiAN
33
98.000
14.90
10.50
69.00
DDB-I (ours)
86.39= 0.74
1.85+ 3.21
0.52: 1338
0.12s 1.56
DDB-II (ours)
98.561 0.92
2.30 ozu
11.10 1.20
46.70a 2:42
(see Table 3) presenting multiple biases, we adopted the same metrics of [32 (see Suppl. Material, Sec. A.1). DDB-I shows overall the most balanced performance across bias-conflicting samples, reporting improvement over LLE (32) of 0.25% for BG-Gap, 2. 2.18% on CoObj-Gap and 5.78% for BG-CoObj-Gap. DDB-II provides significant improvement over the original LfF, but struggles more with the background-conffecte samples than DDB-I.
4.3 Ablation analysis
In this section, we provide an extensive ablation analysis of DDB main components. Without losing generality, all ablations are performed on Waterbirds and exploit Recipe for model debiasing. Additional ablations on Recipe II can be found in the Supp. Mat. The reported analyses regard several aspects of the data generation process with respect to the Bias Amplifier, the effects of the filtering option in Recipe and the behavior of DDB on unbiased data.
Bias Amplifier data requirements. All our main results are obtained by training a BA on 1000 synthetic images per class, and we want here to measure how this choice impacts its effectiveness. From Table 4 (left), we observe that even 100 synthetic images are enough for good debiasing performance despite not being competitive with top accuracy scores. Increasing the number of training images improves WGA, reaching the best performance for 1000 images per class.
CFG strength and generation bias. The most important free parameter for the CDPM with CFG is the guidance conditioning strength w. Such strength can affect the variety and consistency of generated images. Here, we investigate DDB dependency on this parameter. Table shows a limited impact on debiasing performance. WGA variations along different values equal or larger than are limited, with the best performance obtained for w
Table 4: Ablations on: (left) synthetic image count for training the BA on Waterbirds; (right) Impact of guidance strength CDPM sampling, evaluated on Waterbirds. For both experiments, employ Recipe I.
# Synth. images for BA training
Guidance strength
Imgs. 100 200 500 1000
2000
W
1
WGA 86.25 87.10 10 87.67 89.05 90.81
WGA 87.85 90.81 89.25
89.56 88.32
Group extraction via BA plain error sets. In Sec. 3 we describe how we filter the BA error set through a simple heuristic based on per-sample loss distribution. In Tables Ta Ib] 2a and [2b the last row (DDB-I w/ plain etr. set) reports our obtained results when considering as bias bias-conflicting all the samples misclassified by the Bias Amplifier. Our results show trade-off between the two alternatives. The entire set of misclassifications always enough to reach high bias mitigation performance with less critical settings (e.g., like in Waterbirds, p 0.950), gaining an advantage from such coarser selection. On the other hand, the importance of more precise selection highlighted by the superior performance of the filtered alternative in the more challenging scenarios, like in BFFHQ (p 0.995).

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Bins-Identiffcation Accuracy. To frame the goodness of our BA in correctly identifying aligned and conflicting training samples, we provide the accuracy of such a process when considering samples misclassified by it. Specifically, we reach a high bias-identification accuracy of 89.00% for Waterbirds and 96.00% on BFFHQ, supporting the effectiveness of the proposed protocol, which is not dependent on any careful regularization, bias-annotated validation sets, or large ensembles of auxiliary classifiers.
DDB impact on unbiased dataset. In real-world settings, it is generally unknown whether bias is present in datasets or not. Consequently, an effective approach, while mitigating bias dependency in the case of biased dataset, should also not degrade the performance when the bias is not present. To evaluate DDB in this regard, we apply the entire pipeline (adopting Recipe D) on a common image dataset such CIFAR-10 10 2771 comparing it with traditional ERM training with CE loss. In this controlled case, Recipe provides a test accuracy of 84.16%, with a slight improvement of ~ 1% compared traditional ERM (83.26% 63).6%) employing the same target model (ResNet1 8). This result. shows that our Bias Amplifier can still provide beneficial signals to the target model in standard scenarios, potentially favoring the learning of the most complex samples.
Effectiveness of Synthetic Data for BA Training, A core claim of our work is that training the Bias Amplifier on synthetic data prevents the memorization of real bias-conflicting samples. To validate this, we compare our standard BA (trained on synthetic data) with a BA trained on the original real training data within our two recipes. Using the real dataset significantly degrades performance. For Recipe I. the BA trained on real data for 50 epochs memorized all conflicting samples, leading to empty pseudo-label groups. Training for just one epoch (as in JTT (35) yielded WGA of only 79.43%. For Recipe II, using frozen BA trained on real data resulted in a WGA of 78.45%, far below our proposed method and in line with the original LfF performance. These results empirically confirm that using synthetic data is crucial for creating an effective BA.
5 Conclusions
In this work, we present Diffusing DeBias (DDB), a novel debiasing framework capable of learning the per-class bias-aligned training data distribution, leveraging CDPM. Synthetic images sampled from the inferred biased distribution are used to train a Bias Amplifier, employed to provide a supervisory signal for training debiased model. The usage of synthetic bias-aligned data is empirically shown to prevent the detrimental effects typically impacting auxiliary models' training in unsupervised debiasing schemes, such bias-conf sample overfitting and interference, which lead to suboptimal generalization performance of the debiased target model. Our experiments confirm that bias amplifier trained on real data fails this task, while our approach creates a robust and effective supervisory signal. DDB is versatile, acting as a plug-in for unsupervised debiasing methods, since its Bias Amplifier can be incorporated into several debiasing approaches. In this work, we incorporate DDB's 's Bias Amplifier in a two-step and an end-to-end debiasing recipes, outperforming state-of-the-at works on popular biased benchmark datasets (considering single and multiple biases), including recent works relying on vision- laingn- models [25) 6) Finally, while effectively mitigating bias dependency for biased datasets, DDB does not degrade performance when used with unbiased data, making it suitable to be adopted as an effective general-purpose standard training procedure.
Limitations. DDB's main limitations stem from the high computational cost of training diffusion models. As a trade-off between efficiency and generation quality, we reduce the training image resolution to 64 x 64; nonetheless, training our CDPM still requires 14 hours on an NVIDIA A30 GPU with 24 GB of VRAM for the largest considered dataset (BFFHQ, 19,200 images). Our ablations show, however, that this cost can be substantially reduced by training for fewer iterations or lower resolutions (e.g., 3.2 hours for Waterbirds 32 x 32) with only minor impact on final performance. Importantly, this computational cost should be weighed against the significant, often impractical, burden of manually annotating a bias-aware validation set, which many alternative methods require [34, 43]. Another limitation inherited from diffusion models is that generation quality may decrease if the training dataset is too small, potentially limiting DDB's applicability in very small-scale settings. Finally, while we have benchmarked DDB on wide range of standard datasets, the bias mitigation community currently lacks million-scale datasets with appropriate bias annotations for large-scale evaluation, which remains important direction for future research.
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Acknowledgements. We acknowledge the financial support from PNRR MUR Project PE0000013 "Future Artificial Intelligence Research (FAIR)", funded by the European Union NextGenerationEUU, CUP 1332400320004000000 CUP C63C22000770006.
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A Technical Appendices and Supplementary Material
A.1 Datasets
Waterbirds 43 is an image dataset exhibiting strong correlations (p = 0.950) between bird species and background environments (e.g., water vs. land). has 4,795 training images, 1,199 images for validation, and 5,794 for testing.
BFFHQ [22 builds upon FFHQ 20 by introducing demographic biases for face recognition, while BAR [39 is crafted challenge action recognition models that associate specific actions with particular stereotypical contexts. For these two last datasets, we specifically refer to their original versions introduced in [39] and [22], respectively: BFFHQ has a total of 19,200 training images where the semantic classes E {young, old], the bias attribute b E (female, male), and the bias ratio p 0.995. Then, it provides 1,000 validation images (all bias-aligned) and 1,000 testing images with uniformly distributed labels and bias attributes.
We utilize the original BAR version introduced in [39]. The dataset presents 6 different classes, but does not provide explicit bias annotations or a validation set, with 1,941 images for training and 654 for testing.
ImageNet-A consists of 7,500 real-world images from 200-class subset of ImageNet, where deep learning models consistently struggle to make correct predictions. Originally introduced by Hendrycks et al. [14] to evaluate adversarial robustness, it is also employed as a model debiasing benchmark, usually as a bias-conflicting evaluation set for models trained on ImageNet-9 [2] a collection of images with 57,200 samples where categories are super-classes of ImageNet, put together to exhibit textural and shape biases (2. As such, both our CDPM and Bias Amplifier are trained on ImageNet-9. Then, we evaluate both recipes on ImageNet-A in terms of Average Accuracy, following the same protocol of 2 123 38
UrbanCars has been released in 32 and contains 8000 training images with the target represented by urban and country cars. The dataset is built starting from the original 196 classes of Stanford Cars (26). while synthetically incorporating two different shortcuts, background, from the Places dataset [54], and Co-Occurring Objects (CoObj), from the LVIS one [13). In UrbanCars, 90.25% of samples present both background and CoObj attributes aligned with the target class biases, 9.5% of training samples are conflicting with respect to only one of the two attributes, while 0.25% are conflicting to both. The evaluation metrics employed for this datasets are the one introduced in (32), namely ID,ACC, BG-Gap, CoObj-Gap, BaC. ID.ACC is the in-distribution accuracy (the weighted per-class accuracy using the training set per-group subpopulation proportions). The other metrics represent the test accuracy gap of each minority subgroup with respect to ID.ACC.
A.2 Implementation Details
In our experiments, we generate 1000 synthetic images for each target class using a CDPM imple- mented as multi-scale UNet incorporating temporal and conditional embeddings for processing timestep information and class labels. For computational efficiency, input images are resized to 64 64 pixels. Input images are normalized with respect to ImageNet's mean and standard deviation. The CDPM is trained from scratch for 70, 000, 150, 000, 200, 000, 100.000, 100.000 iterations for the datasets BAR, Waterbirds, BFFHQ, ImageNet-9, and UrbanCars respectively. Batch size is set to 32, and the diffusion process is executed over 1, 000 steps with a linear noise schedule (where 81 le-1 and 87 = 0.028). Optimization is performed using MSE loss and AdamW optimizer. with an initial learning rate of 1e adjusted by Cosine Annealinnl (36) scheduler with warm-up period for the first 10% of the total iterations. As per the Bias Amplifier training, we use synthetic images from CDPM. A Densenet- 121 (17] with ImageNet pre-training is employed across all datasets, featuring a single-layer linear classification head. The training uses regular Cross- Easso-y loss function and the AdamW optimizer (37). For BFFHQ, BAR, and UrbanCars, the learning rate is 14_ and 5e for Waterbirds, and ImageNet-9. Training spans 50 epochs with weight decay = 0.01 except for BFFHQ, which uses À = 1.0 for 100 epochs. We apply standard basic data augmentation strategies (following 22 39), ). including random crops and horizontal flips. For our debiasing recipes, we rely on the same backbones used in the existing literature, Res.Ret 18 for BFFHQ, BAR, ImageNet-9/A.
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32 In Recipe II. (Equation B is set to we do not rely on validation set for regularization. This choice is related to real-world application suitability, where no guarantees exist regarding the degree of spurious correlations present held-out subset of data used as validation, possibly resulting in detrimental regularization (
and ResNet-50 for Waterbirds and UrbanCars [39 22 25
in all our main experiments. Finally, similarly to 33
A.3 Full Implementation Details of Recipe (two-step)
For the hyperparameters of Recipe I, we use for each dataset the following configurations. BAR:
batch size = 32, learning rate = 5 x e_ weight decay 0.01, training epochs = 80. Waterbirds: batch size 128, learning
e-t, weight decay 1.0, training for 60 epochs. BFFHQ:
batch size 256, learning rate = 5 x e weight decay = 1.0, training for 60 epochs. The GDRO flags.
optimization always run with the -robust and -eweiggttggrooeeeeeeeee
For UrbanCars, presenting two biases, we apply Recipe as is, considering the filtered correct and wrong classifications in each class 4 subgroups during GDRO optimization. We then consider all the available subgroups at evaluation time.
A.4 Full Implementation Details of Recipe (end-to-end)
For BAR, we use the same parameters reported in 339 batch size = 128, learning rate = x e weight decay 0.01, training epochs = 50. Waterbirds: batch size 128, learning rate = 5 x e weight decay = 0.01, training epochs 80. BFFHQ: batch size 256, learning rate = 5 x e-b, weight decay = 0,01, training for 50 epochs. For these recipes, we accumulate gradients for 8 iterations (16 for BFTHQ) with mini-batches made of 16 samples.
A.5 Ablation Studies with DDB's Recipe II
This appendix section focuses additional ablation studies, dedicated Recipe II. This set of ablations aims at providing additional evidence of Recipe II's robustness and effectiveness, similar to what shown in Sec. |..3 of the main paper regarding Recipe As in the main paper, we run all our ablations on Waterbirds.
Table 5: Ablation on the number of training images for the Bias Amplifier, in terms of WGA on Waterbirds, when using DDB's Recipe II.
Synth. images for BA training
Imgs.
100
200
500
1000
2000
WGA 84.42 90.03 90.18 90.78 91.56
Bias amplifier data requirements. This ablation study measures how the number of training images for the Bias Amplifier impacts DDB's 's Recipe II. Results are evaluated in terms of WGA on Waterbirds (see Table 4). 200 images (100 images per class) are enough to obtain state-of-the-art competitive results (WGA equal to 90.03), with an increasing number of images corresponding to higher WGA values, up to a maximum 91.56 for 2000 total images.
Table 6: Impact of guidance strength w on the final WGA on Waterbirds, when using DDB's Recipe II.
Guidance Strength
2
5
WGA 87.07 91. 01.56 88.01 89.87 88.30
Here, we evaluate DDB's Recipe II dependency on the guidance strength parameter. Results are summarized in T The best results correspond to the usage of w 1, with different guidance strength parameter values having a relatively limited impact on debiasing performance.
CFG strength and generation bias.
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DDB impact on an unbiased dataset. Similarly to what is shown in Sec. 4.3 we want to ensure that applying Recipe II on an unbiased dataset does not degrade the final performance. Also Recipe demonstrates to be applicable in unbiased scenarios, as it measures a final test accuracy of 83.87% on CIFAR1O, which is slightly higher than the ERM baseline (83.26%).
DDB with less biased datasets. We evaluated DDB's robustness on datasets with weaker spurious correlations by resampling Waterbirds create versions with p E {0.90, 0,80, 0.703. Table shows that while the ERM baseline improves as the bias weakens, DDB-I consistently provides a significant performance boost across all bias levels, demonstrating its effectiveness even when the bias less pronounced.
Table 7: WGA (%) Waterbirds with varying bias correlation (p).
Method p 0.95 p 0.90 p 0.80 p -= 0.70
ERM
62.60
63.40
64. 12
68.84
DDB-I
90.81
86.91
90.19
86.14
Performance on an alternative version of BAR with bias annotations. We tested out method on an alternative version of the BAR dataset, specifically the one introduced in [29] and having P 0.95. Table & shows how even for this other version of BAR, our method is effective mitigating bias dependency. When applying Recipe-l we obtain SOTA performance, confirming the good results already obtained for the benchmark dataset in the main paper.
Table 8: Results on the annotated BAR dataset (p 0.95).
Method
Average Accuracy
ERM
82,40%
ETF-Debias [46]
83.66%
DisEnt+BB [28]
84, 96%
DDB-I (Ours) 85.36 % 0.71
Impact of Generated Image Quality. We studied the trade-off between the visual quality of generated images, computational cost, and debiasing performance on BFFHQ. We trained the CDPM for fewer iterations, resulting images with higher (worse) Fréchet Inception Distance (FID) scores. As shown in Table performance degrades gracefully as image quality decreases. Even with a CDPM trained for only 17k iterations (FID of 30.80), the final conflicting accuracy remains competitive with the state-of- sitie--f-thee-e aht and takes only ~9.5 hours to train. This supports our claim that photorealistic quality is secondary to capturing the essential bias cues; even images with artifacts contain the necessary signals for our BA to function effectively.
Table 9: Impact of CDPM training iterations FID and final confticting accuracy (%) BFFHQ with Recipe
CDPM Iters
FID (lower is better) Conflicting Acc.
200k (original)
22.60
74.67 2.37
170k
30.80
73.70 + 0.71
120k
36.39
71.20 + 1.13
70k
41.82
67.40 + 2.55
A.6 Additional Biased Generations
In this Section, we present 100 additional per class generated images for each dataset utilized in the study, as produced by our CDPM. In the Waterbirds dataset (Figure(4) the model adeptly captures and replicates the correlation between bird species and their background. In the BFFHQ dataset (Figure 8). it effectively reflects demographic biases by mirroring gender-appearance correlations. The results from the BAR dataset (Figure ? exhibit the model's 's adeptness in handling multiple bias patterns, preserving typical environmental contexts for different actions. Finally, the ImageNet-9 datasct (Figure 6 stereotypes the various classes and their backgrounds well. Overall, the model
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generates high-quality samples that are fidelity-biased biased across all datasets. Our research confirms that the model exhibits bias-learnngg behavior, as it not only learns but also amplifies existing biases within diverse datasets. We leverage this characteristic to produce synthetic biased data aimed at enhancing the robustness of debiasing techniques.
Figure 4: Synthetic bias-aligned image generations for each class of the Waterbirds dataset.
Each grid displays 100 synthetic images per class, highlighting model bias alignment.
Figure 5: Synthetic bias-aligned image generations for each class of the Urban dataset. Each grid displays 100 synthetic images per class, highlighting model bias alignment.
A.7 Quantitative Analysis of Bias Amplification
To empirically verify our hypothesis that CDPM amplifies the bias present in a training dataset, conducted quantitative analysis on Waterbirds. We created training sets with varying degrees of spurious correlation (p {0.95, 0.90, 0.80, 0.70)) and trained separate CDPM each. We then generated 1,000 synthetic images per class from each trained model. These synthetic datasets were manually annotated to estimate the resulting bias correlation (Payath)-
As shown in Table TO the CDPM consistently generated dataset with higher bias ratio than the one it trained on. For example, when trained on data with p 0.95, the generated samples
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Figure 6: Synthetic blasealigned image generations each class of the BAR dataset. Each grid displays 100 synthetic images per class, highlighting model bias alignment.
exhibited a correlation of Paynts = 0.99. This empirically demonstrates the bias-amplifying property of diffusion models. This behavior can be attributed to the models learning the density of the training data and, during sampling, having a higher probability of generating samples from the high-density regions [23], which correspond the majority bias-aligned groups each class. This amplification is precisely what allows our BA to learn a robust representation of the bias without being exposed to the rare, real chaffccnni samples.
Table 10: Bias amplification synthetic data generated by a CDPM trained on Waterbirds with varying training bias ratios (Ptrain).
Ptrain 0.950 0.900 0.800 0.700
Psynth 0.990 0.961 0.912 0.827
A.8 Bias Validation via Captions in Synthetic Images
As ulterior confirmation of bias in CDPM-generated images, besides two independent annotators. we implement an unsupervised analysis pipeline using the pre-trained BLIP-2 Flan'T5 x model [30] for zero-shot image captioning without any task-specific prompting, for avoiding any guidance in the descriptions of the synthetic data. Our method processes the resulting captions through stop word removal and frequency analysis reveal underfying biases without relying on predetermined categories supervised classification models. Figure presents the keyword frequency distribution for the Waterbirds dataset, where the most prevalent terms naturally correspond known bias attributes (c.g., environmental contexts). These findings confirm that generated images are bias- aligned. Furthermore, the obtained word frequency histograms suggest that images generated by DDB's bias diffusion step may provide information useful to support bias identification (or discovery) the dataset level.
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Figure 7: Synthetic bias-aligned image generations for each class of the ImageNet-9 dataset.
Each grid displays 100 synthetic images per class, highlighting model bias alignment.
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Figure 8: Synthetic bias-aligned image generations for each class of the BFFHQ dataset. Each Frequency (%)
grid displays 100 synthetic images per class, highlighting model bias alignment.
30 29.1%
23. 23.4%
21.4%
Frequency (%)
25
20
17.8%
13.1%
13.1%
10.2%
10.0%
3.8%
6.5%
5.1%
5.1%
4.4%
5
5.6%
9%
4.7%
3.0%
3.0%
0
water
bird
standing
flying
rock
white
boat
painting
background
man
bird
sitting
tree
forest
branch
standing
woods
yellow
grass
flying
Words
Words
Figure 9: Word frequency analysis from 1000 generated captions for Waterbirds classes 'landbird'
(left) and 'waterbird' (right), showing top 10 most frequent terms.
28


Reviews:
[{"review_id": "2jsN0zIu3i", "reviewer": "Reviewer_EHbf", "summary": "This paper introduces Diffusing DeBias (DDB), a novel unsupervised debiasing framework that leverages Conditional Diffusion Probabilistic Models (CDPMs) to synthesize bias-aligned images. These synthetic images are used to train a bias amplifier model, which acts as a supervisory signal in downstream debiasing approaches. The key innovation is to amplify the spurious correlations typically considered harmful in generative models and use this amplified bias as a tool for model debiasing. Two strategies are proposed: 1) DDB-I (Recipe I): A two-step method where the bias amplifier pseudo-labels real training data, which are then used in a Group-DRO optimization. 2) DDB-II (Recipe II): An end-to-end scheme where the bias amplifier guides sample weighting during training. The method is evaluated on several benchmark datasets (Waterbirds, BFFHQ, BAR, UrbanCars, ImageNet-A) and shows strong performance in terms of worst-group accuracy and fairness gaps, often surpassing both supervised and unsupervised baselines.", "questions": "Overall, I think the method achieves strong empirical results across a variety of benchmarks. However, concerns remain regarding computational cost, generalization to weaker or more complex biases, and reliance on synthetic data quality. Also, I do believe that it is necessary to perform experiments on larger scale datasets.", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## Strengths\n\n* The Bias Amplifier module can be integrated into both two-step and end-to-end debiasing approaches.\n* Empirical Performance: Achieves strong results on a wide variety of benchmarks and bias types (single and multiple spurious correlations).\n\n---\n\n## Weaknesses\n\n* Training diffusion models is expensive (\\~14 hours on an A30 GPU for BFFHQ at 64×64 resolution), which may limit scalability.\n* The method's effectiveness may diminish when spurious correlations are weak (ρ < 0.95) or when multiple subtle biases coexist.\n* *Since synthetic data are sampled from biased distributions, the generated data might lack semantic diversity or introduce synthetic artifacts.\n* Limited Real-World Scalability Demonstration: Most experiments are on controlled benchmark datasets. Less is said about generalization to large-scale or long-tail distributions.", "was_revised": true, "final_justification": "I appreciate the authors' rebuttal and the additional results. However, the new evidence is not sufficiently convincing to change my assessment. The method still lacks evaluation on large-scale, non-curated datasets that better reflect real-world distributions, and there is no analysis of robustness across model sizes and training-data. Including these evaluations, or at least acknowledging them as limitations and outlining them as future work, would strengthen the paper.\n\nMoreover, as other reviewers noted, while the authors claim that diffusion models tend to generate samples from majority groups and thereby amplify bias, this tendency is also observed in other generative methods (e.g., GANs), so it is not unique to diffusion models.\n\nGiven these unresolved concerns, I will maintain my initial score, and I encourage the authors to further polish the manuscript.", "rebuttal": "We thank the reviewer for the comments and for appreciating our empirical performance and the flexibility of the integration of the bias amplifier model. We will now provide a point-to-point response to the raised concerns. References follow the same numbering as the submitted paper. \n\n**Training diffusion models is expensive (~14 hours on an A30 GPU for BFFHQ at 64×64 resolution), which may limit scalability.**\n\nWe agree that this is a limitation of our proposed approach, and in fact, we explicitly declared the training time as a possible limitation of our work. However, we also would like to point out that training a diffusion model has nowadays to be considered acceptable and in line with current standards in computer vision and deep learning computational requirements. It is also worth to notice that our reported computational times are referred to a basic implementation of CDPM, as tackling the training time of diffusion models is secondary to our main objective, which aims at showing how a CDPM can be effectively used to generate bias-aligned samples to replace the original training set for training biased auxiliary models, in order to solve by construction some critical open challenges in debiasing scenarios, such as memorization of the few bias-conflicting samples, and when to stop the training of such auxiliary models. \n\nNonetheless, we reported a new experiment on Waterbirds dataset, where the resolution was reduced to 32x32, obtaining a comparable WGA (90.09%, with respect to 90.81% of the original experiment using 64x64 image resolution with Recipe I), with the training time reduced to 3.2 hours (from an initial training time of roughly 14 hours), showing a significant reduction of the computational time with a negligible decrease of the performance. \nPlease refer to the first response to Reviewer 4iDf for more details on this experiment.\n\n\n**The method's effectiveness may diminish when spurious correlations are weak (ρ < 0.95) or when multiple subtle biases coexist.**\n\nIn this work, we employ the main benchmark datasets utilized in the image classification model debiasing community. Specifically, all the works on model debiasing that we reference in our paper and benchmark against, exploit a minimum degree of correlation ρ >= 0.95, as the main focus is on mitigating bias in models when the training distribution is predominantly biased. As such, more than a design choice is a convention adopted in this community for benchmarking debiasing algorithms (e.g., see [28, 33, 22, 18, 17, 46, 40]). Furthermore, we can reasonably expect that the generalization of a naively trained ERM model improves naturally when trained on a less severe biased dataset (with ρ < 0.95). \n\nHowever, we agree that it is interesting to evaluate our DDB approach on a less-biased dataset, and thus, we perform a dedicated set of experiments, resampling the original Waterbirds dataset to obtain three alternative versions with lower amount of training bias-aligned samples (i.e., ρ=0.90, ρ = 0.80, and ρ=0.70). Therefore, we trained both a vanilla model (as a baseline) and our DDB with Recipe I (i.e., bias amplifier + G-DRO), reporting the obtained results in the following table:\nMethod | $\\rho=0.95$| $\\rho=0.90$| $\\rho=0.80$| $\\rho=0.70$|\n|:-------------|:------------------:|:-----------------------:|:-------------------------:|:------------------:|         \nERM\t|\t62.60%\t|\t63.40%\t\t|\t64.12%   \t|  68.84%\t|  \nDDB-I \t|90.81%|\t86.91%\t\t|\t90.19%\t|  86.14%|  \n\nAs we can see, DDB is still capable of obtaining compelling performance, with a significant improvement over the ERM model. Furthermore, the ERM model test accuracy increases when ρ decreases, as expected. \n\nAdditionally, in the submitted version of the paper, we provided a dedicated experiment (DDB impact on unbiased dataset, already present in the submitted version) to evaluate the performance of DDB  when no bias is known to be present at all (ρ=0), utilizing CIFAR-10, and obtaining about +1% in accuracy with respect to the ERM.  \n\nFinally, regarding multiple biases, we reported the results obtained on UrbanCars, which is a reference benchmark for testing multiple biases in the debiasing community. Specifically, it is constructed to have two different bias attributes (i.e., background and co-occurring objects). The obtained results (following the metrics introduced in the original paper proposing the dataset [31]) show how DDB is actually capable of handling more than one bias. For the latter case, with Recipe I (BA + G-DRO) we manage to reduce the generalization gap with respect to both bias attributes to less than 2\\%. In contrast, most of the other unsupervised debiasing methods struggle to mitigate model dependency on both.\n\nWe hope that this response clarifies the reviewer’s doubts, and we will include the additional results on Waterbirds in the final version of our manuscript. \n\n\n**Limited Real-World Scalability Demonstration: Most experiments are on controlled benchmark datasets. Less is said about generalization to large-scale or long-tail distributions.**\n\nConnected to our previous answers above, in this work we employed the main benchmark datasets utilized in the image classification model debiasing community (e.g., [28, 33, 22, 18, 17, 46, 40]), which are not large scale. To test for real-world datasets, we utilized BFFHQ and ImageNet-9/A. In this sense, we are not aware of a truly large-scale dataset (e.g., million sized) employed for evaluating debiasing methods. \nAbout long-tail problems, we argue that evaluating such problems, even if interesting, is not in the scope of this work. \n\n\n**Since synthetic data are sampled from biased distributions, the generated data might lack semantic diversity or introduce synthetic artifacts**\n\nWe apologize for our lack of clarity on this point, and we thank the reviewer for raising this comment. \nIndeed, bias is by definition a spurious correlation between target labels and data. In a certain sense, biased (a.k.a. bias-aligned) images may be seen as \"stereotyped\", with respect to the bias attributes they share. \n\nAs such, our initial hypothesis was that, when learning the density of the training distribution with a conditional diffusion model, the bias may dominate the generated images' semantic attributes. Since our final aim is to exploit the generated images to train a bias amplifier, having \"stereotyped\" images may actually be beneficial for amplifying the bias. \n\nRegarding artifacts, we expect that, when bias attributes are present, a trained model ends up to be strongly relying on bias shortcuts for making its predictions, giving less importance to semantic attributes and potential randomly introduced artifacts. \n\nThe reported empirical results support our intuitions, as the usage of DDB makes both our recipes better than the original counterparts, and competitive with existing SOTA methods."}, {"review_id": "w5SsddwJEr", "reviewer": "Reviewer_89yi", "summary": "This paper introduces Diffusing DeBias (DDB), a novel framework for unsupervised model debiasing that leverages conditional diffusion models to synthetically generate bias-aligned data. The core idea is to sidestep the contamination introduced by rare bias-conflicting samples in training by training a Bias Amplifier (BA) model solely on synthetically generated, bias-amplified data. DDB is plugged into two debiasing paradigms: a two-step method (DDB-I) and an end-to-end training scheme (DDB-II). The method is evaluated on several standard benchmarks, including Waterbirds, BFFHQ, BAR, and UrbanCars, showing improvements over existing baselines.", "questions": "Please see the Weaknesses.", "limitations": "The authors properly address the limitations in Section 5.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- **Strengths**\n    1. The idea of bias amplification through synthesis is a refreshing and original perspective.\n    2. The framework’s ability to plug into both two-step and end-to-end approaches enhances its generalizability and potential for adoption.\n    3. The experiments are conducted on a diverse set of datasets and baseline methods, supporting the robustness of the evaluation.\n    \n- **Weaknesses**\n    1. The paper does not appear to include experiments that directly assess the effectiveness of the Bias Amplifier (BA) when trained solely on bias-aligned samples generated by the diffusion model. A comparison between a BA trained on the original training dataset and one trained on synthetic bias-aligned samples—using an otherwise identical debiasing setup—would be valuable. Such an experiment would provide a more rigorous validation of the proposed method’s effectiveness in mitigating bias. (Note: While DDB-II adopts the debiasing strategy of LfF, it differs in that it uses a *frozen* BA to train the debiasing model, whereas LfF jointly trains both components. As such, a direct performance comparison between DDB-II and LfF may not offer clear insights into the impact of synthetic data or the BA training design.)\n    2. The performance table presented in Table 1 is not well organized, and nearly half of the cells lack reported performance values.\n    3. To the best of my knowledge, the training set of BAR consists solely of bias-aligned samples [1], which—as the authors note—makes it suitable for training a robust bias amplifier. Given this condition, performance differences among debiasing methods on BAR are likely attributable to the use of synthetic data and differing debiasing strategies. Therefore, the BAR dataset may not be well-suited for evaluating the effectiveness of the proposed method (*Diffusing the Bias*).\n\n[1] Nam et al., “Learning from Failure: Training Debiased Classifier from Biased Classifier”, NeurIPS 2020.", "was_revised": true, "final_justification": "Through the discussion, the authors provided clear responses regarding the consistency between the proposed method and the experimental setup used to support it. They also demonstrated the effectiveness of their approach with well-designed additional ablation studies. Most notably, to the best of my knowledge, the idea of bias amplification through synthesis is novel and has not been previously proposed.", "rebuttal": "We thank the reviewer for the insightful comments and for recognizing our main idea as original and refreshing. We will now provide a point-to-point response to the raised concerns. References follow the same numbering as the submitted paper. \n\n**W1. The paper does not appear to include experiments that directly assess the effectiveness of the Bias Amplifier (BA) when trained solely on bias-aligned samples generated by the diffusion model. A comparison between a BA trained on the original training dataset and one trained on synthetic bias-aligned samples—using an otherwise identical debiasing setup—would be valuable. ...**\n\nWe thank the reviewer for the comment. Employing Waterbirds (as done with the other ablation studies), we performed the suggested experiments as follows. First, we train the BA on the original dataset, then we employ it for our two Recipes, as follows:\n\n- Frozen BA + LfF (Learning from Failure [38]), that is our Recipe II but with the BA trained on the original real training images (not the diffusion-generated synthetic ones). In this case, we obtain a Worst Group Accuracy (WGA) of 78.45 +/- 2.61 over three runs, with an average decrease of 13.11%. Furthermore, when comparing these results with the original LfF, whose auxiliary biased model is not frozen as the reviewer correctly noted, we got a WGA of 78.00%, which is in line with the result of our new performed experiment, still much less than the performance of our proposed approach. \n\n- With the same BA trained on the original images, we applied our Recipe I, i.e., training GroupDRO with the group annotations obtained as in the Section 3.3.1 of the submitted paper. Here, the results confirm the problem of memorization that we highlighted in our paper. In fact, training the BA on the original images (same setting of our original experiment, which is 50 epochs) results in 100% training accuracy, hence with bias-conflicting samples totally memorized. \nAs such, when mining the group pseudo-labels, we end up with two empty sets (corresponding to conflicting in class 0 and class 1). These results confirm that, indeed, memorization is a critical issue when training biased auxiliary models. Nonetheless, to still obtain a somewhat meaningful comparison, we trained the bias amplifier on the original images for only one epoch (as done in JTT [34]), obtaining a final WGA of 79.43 +/- 1.92, again, with a significantly lower mitigation performance than exploiting the BA trained with our original approach. \n\nWe will include these results in the final version of our paper, and we thank the reviewer again for suggesting these comparisons to strengthen and add value to our contribution. \n\n\n**W2. The performance table presented in Table 1 is not well organized, and nearly half of the cells lack reported performance values.**\n\nWe agree with the reviewer that Table 1 should be improved. The lacking values are due to benchmark methods missing some of the considered datasets. Hence, we plan to divide Table 1 per dataset to have a better organization and readability. \n\n\n**W3. To the best of my knowledge, the training set of BAR consists solely of bias-aligned samples [1], which—as the authors note—makes it suitable for training a robust bias amplifier. ...**\n\nWe personally inspected the original version of the BAR dataset (from Nam et al., 2020 [38]), and we can confirm that a few bias-conflicting samples are present in the training set (for instance, see samples with original filenames in the training folder *diving_70, diving_89, diving_99*), with an imbalanced distribution with respect to the classes.\n\nHowever, as the original version of BAR does not provide bias annotations, we performed additional experiments, employing the BAR version introduced in Lee et al., 2023 [28]. Here, two versions with full bias annotation of BAR are provided (ρ = 0.95 and ρ = 0.99). In the rebuttal time, we managed to perform an experiment on this dataset utilizing the variant with $\\rho=0.95$. Here we report the results obtained with our DDB-I (Recipe I), comparing against two SOTA methods on this dataset. \n\n\nMethod | Average Accuracy |  \n|:---------------------|:---------------:|  \n|ERM              | 82.40 |  \n|ETF-Debias[46] | 83.66 |  \n|DisEnt+BE [28] | 84.96  |  \n|DDB-I (Ours) | **85.36 +/- 0.71**|  \n\n\nThese results confirm the effectiveness of the proposed approach, and we are confident that they can have solved the reviewer's doubts.\n\nWe remain at disposal for further information or clarifications on that."}, {"review_id": "B5Hh4X1mst", "reviewer": "Reviewer_4iDf", "summary": "The authors proposed Diffusing DeBias, which aims to mitigate the dataset bias by amplifying the bias for the auxiliary biased model with newly generated synthetic data. The generated dataset could be used for methods for both 2-step methods (Group DRO) and end-to-end methods (e.g., LfF). At a high level, bias-aligned samples are generated via classifier-free guidance using a biased training dataset. The authors demonstrated the superiority of the proposed method over existing debiasing methods using 4 different datasets.", "questions": "Please refer to the weaknesses.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n- [Scalability] The proposed method could be applied to existing methods that utilize the bias amplification module. Generally, debiasing methods utilize an auxiliary model that focuses on learning the bias attribute. The authors demonstrated that the proposed method could be applied to both 2-step and end-to-end debiasing methods, which seems scalable and convincing.\n- [Clarity and organization] The paper is well-written and easy to follow.\n\nWeaknesses\n- [Quality of generated images] The visual quality of the generated images, particularly those from real-world datasets such as BAR (Figure 3), appears somewhat unpolished and needs improvement. Including quantitative image quality metrics (e.g., FID, LPIPS) would strengthen the evaluation of the generated images. Furthermore, selectively incorporating high-quality synthetic images based on such metrics could potentially enhance performance. It would be beneficial for the authors to explore whether performance improves by filtering generated samples using the image quality metrics.\n- [Computational complexity] The authors stated that high computational complexity is one of the limitations. However, I believe that this limitation is a big issue compared to the performance gain the proposed method brings. I question how practical such an approach would be for removing the bias of large-scale datasets. While the authors tried their best to improve the efficiency by reducing the resolution to 64x64, it degrades the quality of generated images, as stated by the authors. A more thorough discussion or empirical analysis of this trade-off would be valuable.\n- [DDB with less biased dataset] Most experiments are conducted on datasets with high bias severity, which facilitates the generation of strongly bias-aligned samples. It remains unclear whether DDB is effective when applied to datasets with subtler or less explicit biases. The authors are encouraged to evaluate the method on datasets with lower bias severity to assess its robustness and generalizability in such settings.\n- [Comparisons with image-generation studies] Although the authors reference image-generation-based debiasing methods such as Biaswap [1], they do not include direct comparisons. Given that Biaswap also employs image translation techniques to mitigate bias, a comparative analysis would provide a more comprehensive evaluation of DDB’s relative strengths and limitations. Including such baselines (e.g., ActGen [2]) in the experimental section would significantly improve the paper.\n\n[1] BiaSwap: Removing dataset bias with bias-tailored swapping augmentation (ICCV 2021) \n\n[2] Active Generation for Image Classification (ECCV 2024)", "was_revised": true, "final_justification": "I read the author's response and my issues are mostly resolved. Although there are two points still remain controversial, I believe leaving them as the limitation and future work would be helpful for the community. Thus, I raised my score from Borderline reject to Borderline accept.", "rebuttal": "We thank the reviewer for the comments, and for appreciating our main idea, its flexibility to be used in both 2-step and end-to-end debiasing methods, as well as the clarity of the exposition. We will now provide a punctual response to the raised concerns. References follow the same numbering as the submitted paper. \n\n**W1 [Quality of generated images] ... and W2 [Computational complexity] ...**\n\nIn our experimental stage, we include an extensive and diverse set of benchmark datasets for model debiasing, and we are not aware of a truly large-scale benchmark with proper annotations allowing for bias-mitigation analyses (e.g., equipped with a test set with subgroup annotations). Moreover, we would like to highlight that our performance gain is not negligible, given that we are able to achieve SOTA results across quasi-realistic, real-world data, and multiple bias benchmark datasets, while most of the other (bias) unsupervised debiasing methods struggle in at least one of these settings [31]. \n\nAs per the resolution of the synthetic generated data, we indeed highlight that we generate images at a resolution of 64x64 pixels to reduce computational time, regardless of the quality of the generated data (as measured by FID or other metrics). \nHowever, this parameter is not fundamental for our method as we are not interested in perfect fidelity or high quality appearance, but rather in capturing the bias cues in place of the semantic attributes and, as such, we do not mind if some image artifact may be introduced. \nRather, this is a pro of our approach, as we are not constrained to generate high-quality, perceptually highly realistic images. \n\nTo further support this statement, we performed an experiment on Waterbirds data, computing the Fréchet Inception Distance (FID) metric to evaluate the quality of generated images. Specifically, we trained our CDPM on Waterbirds using a pixel resolution of 32x32, obtaining a FID of 58.11, while our original generated images at 64x64 showed a FID of 39.03 (the lower, the better). Exploiting these generated images, we then run our Recipe I (BA + G-DRO): the obtained WGA results 90.09 +/- 0.55% over three independent runs, less than 1% lower than our reported main result of 90.81 +/- 0.68 %. \nThe fact that even a low resolution of 32x32 is sufficient to capture the existing shortcuts should further be a reasonable evidence that the quality of the obtained generations is secondary to the final aim of our paper.\n\nAs an additional analysis on this matter, we performed new experiments exploiting BFFHQ. Specifically, in our original experiments, we trained the CDPM for 200k iterations, obtaining a FID of 22.60. In these additional experiments, we instead utilize intermediate training iterations, from 70k to 170k. As expected, the images generated with these models show a higher FID, and our aim was to assess how this impacts the final results.\nIn the following table, we provide the FID of the generated images and the final resulting Conflicting Accuracy when applying Recipe I (BA+G-DRO). \n\n| # CDPM Iters |   FID   | Conflicting Acc.     |\n|:------------:|:-------:|:---------------------:|\n| 200k (original results)        | 22.60  | 74.67% ± 2.37         |\n| 170k         | 30.80   | 73.70% ± 0.71         |\n| 120k         | 36.39   | 71.20% ± 1.13         |\n| 70k          |  41.82   | 67.40% ± 2.55         |\n\nAs we can notice, even if the FID increases by 36% over the original results, when employing 170k training iterations, the final conflicting accuracy is comparable to the one corresponding to images obtained with a model trained for 200k iterations. \n\nFurthermore, it is worth noticing that we achieve competitive results with respect to the state of the art, even when employing 170k iterations, requiring approximately 9.5 hours of training time.\n\nWhen training the model for 70k iterations only (FID = 41.82), we obtain an average conflicting accuracy of 67.40% (with the best run reaching a 69.2% conflicting accuracy), but requiring less than four hours for training the CDPM. We believe that these results confirm that even if the quality of images decreases, bias is still present in the generated images, and is effectively captured by our BA. \n\n\n**W3. DDB with less biased dataset. Most experiments are conducted on datasets with high bias severity, which facilitates the generation of strongly bias-aligned samples. It remains unclear ...**\n\nWe performed dedicated experiments to clarify this matter. Please refer to the answers we provided to reviewers EHbf and w3kR. \n\nIn summary, in the first reply, we report experiments on multiple versions of Waterbirds data, which we resampled to have $\\rho=0.90$, $\\rho=0.80$, $\\rho=0.70$. In this experiment, we show how our method is always capable of greatly improving over the ERM baseline. In the second response, we also measure the bias correlation found in the generated images when the CDPM is trained on the same resampled versions of Waterbirds data with less extreme bias, confirming how the presence of bias-aligned samples is always amplified in the resulting synthetic generations.\n\n\n**[W4. Comparisons with image-generation studies. Although the authors reference image-generation-based debiasing methods such as Biaswap ...**\n\nWe thank the reviewer for pointing us to other methods for the performance comparison, as well as other works employing generative models to countermeasure generalization failures in image classification (like ActGen).\n\nRegarding the comparison with BiaSwap [21], we provide here a direct comparison between our two debiasing recipes and BiaSwap on two shared benchmark datasets (BAR and BFFHQ).\n\n| Method         | BAR (Average Accuracy) | BFFHQ (Conflicting Accuracy) |\n|:---------------|:----------------------:|:-----------------------------:|\n| BiaSwap        | 52.40%                 | 58.87%                        |\n| DDB-II (ours)  | 72.81%                 | 70.93%                        |\n| DDB-I (ours)   | 70.40%                 | 74.67%                        |\n\nAs evidenced by the significant improvement brought by our approach, the intuition of amplifying bias through bias-aligned synthetically generated images is effective and brings more advantages than relying on bias-amplifying loss functions for the auxiliary model (as commonly done in the literature, including BiaSwap, where the GCE [53] is employed), or by attempting to inject synthetic bias-conflicting characteristics into bias-aligned samples (also done in BiaSwap).\n\nFor the other suggested reference, ActGen, it does not address the bias mitigation problem and adopts different experimental protocols and data, preventing a direct comparison. Nevertheless, we will incorporate it in the Related Work section of the final version of our manuscript, as improving generalization in challenging downstream tasks through synthetic image generation is a topic linked to ours."}, {"review_id": "ZTLgOKzVCI", "reviewer": "Reviewer_w3kR", "summary": "This paper presents debiasing method using biased data generated by biased diffusion model. Specifically, authors proposed a method to amplify the bias leveraging the bias-aligned samples generated by the diffusion model. The method trains bias amplified auxiliary encoder. Then, the main encoder is debiased by the two different strategies. The result verifies the method.", "questions": "Following questions arise:\n\n*1. How does the diffusion model behaves when trained by biased dataset?*\n\nDoes the model imitate the bias in dataset, or amplify the bias? Please provide a quantitative analysis for the bias in generated samples.\n\n*2. Quality of generated samples*\n\nQuantitative evaluation for the image quality of generated samples would improve the academic value of this work. If it has degraded visual quality, it is recommended to justify empirical results, at least in conceptual level. Moreover, exploring the properties of the generated samples would enhance the novelty of this work.", "limitations": "Yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 1, "strengths_and_weaknesses": "###  **Strength**\n- The method suggests novel method to utilize diffusion model as bias-aligned sample generator. \n- The experiments are conducted for various datasets\n- Authors provides abundant ablation study to verify the method \n\n\n-----\n\n### **Weakness**\n\n1. *This work has marginal novelty:*\n\nThe proposed work consists of two components, synthesizing the bias-aligned samples by diffusion model, and debiasing main encoder by bias-amplified encoder. However, both main components are based on existing techniques, making the overall method appear as a simple combination of two methods. \n\n\n2. *More evidence for using diffusion model is recommended:*\n\nAuthors utilized the diffusion model to amplify the bias, which is justified only by the citation of previous works. While it is generally understood that diffusion models are sensitive to data bias, an in-depth investigation for the biased diffusion model is recommended to strengthen the originality of this study. Otherwise, it lowers the novelty of this research, as most of contents are from previous works. For instance, section 3.2 in the manuscript introduces diffusion model which is not novel. \n\nIt is recommended to further investigate how the diffusion model behaves when trained on biased dataset. Specifically, does the model simply reproduce the existing biases, or does it amplify them beyond what is present in the original data? If the model amplifies the bias, any quantitative analysis demonstrating a bias amplification in the generated samples would be valuable. Such exploration is essential, as understanding the biased sample generated by the diffusion model is central to the motivation of this work and constitutes a core component of the proposed method.\n\n3. *Limited discussion on the generated samples:*\n\nThere is limited discussion on the generated bias-aligned samples. They are core in the method, amplifying the bias. Accordingly, in-depth investigations on the synthesized images and their influence to the training are essential. Howerver, the paper only presents few generated samples, which is limited to understand the property of the generated bias-aligned samples. Disussion on the generated samples are limited, for instance, a quantitative analysis on the visual quality. Analysis on the generated samples would help to understand the biased diffusion model, and the proposed framework to reduce the bias.", "was_revised": true, "final_justification": "I have read authors's responses. \n\nGiven that generative model-based approaches are already widely explored in the previous works, the author should provide concrete evidence or analysis to justify the use of a diffusion model over other generative models. In conceptual discussion, authors claim that diffusion model tends to generate samples from majority group, thereby amplifying bias. However, this tendency is also observed in other generation methods, such as GANs, which indicates that the property is not unique to diffusion models. \n\nOverall, the paper employs the diffusion model merely as a tool to generate samples, without introducing any contribution or method to amplify bias of diffusion model. Hence, I carefully determined to maintain my original score.", "rebuttal": "We thank the reviewer for the comments and for recognizing as strengths the novelty of the idea and the quality of the experimental phase including a large ablation analysis. We will provide in the following a punctual response to the raised concerns. References, when applicable, follow the same numbering as in the submitted paper.\n\n**W1. This work has marginal novelty ...**\n\nAs also noted by Reviewer 89yi, our main novelty is the usage of diffusion models for amplifying bias, obtaining synthetic samples that can be used for training a bias amplifier instead of using the original training set. \nUnlike existing works employing data augmentations (e.g., BiaSwap [21] and DFA [27]) or adversarial approaches to synthesize bias-conflicting samples (e.g., BiasAdv (Lim et al, CVPR2023)), to the best of our knowledge, this work is the first to propose the usage of diffusion models for actually generating bias-aligned samples, which are to be used for training an auxiliary biased model. Most notable, our approach solves by construction the problem of training set memorization, which affects the proper learning of an effective auxiliary biased model. \n\nExisting methods often employ bias-annotated validation sets or complicated heuristics [34, 43, 51] for deciding when to stop such training. As a bias-annotated validation set is hard to obtain in real-world applications, we argue that solving this problem is a fundamental novelty of this work. This is also supported by the new performed experiments (please, also refer to the first answer to Reviewer 89yi).\n\nRegarding the debiasing recipes, we exploit a popular end-to-end and a two-stage approach to provide evidence on how the proposed method is generally applicable, and how it can potentially be used as an add-on for existing debiasing approaches, providing significant improvements over the original methods.  Furthermore, in this rebuttal, we included additional experiments empirically showing how diffusion models indeed amplify existing biases, and how this actually helps our debiasing recipes.\n\nIn the end, we would like to respectfully point out that our work is much more than a simple combination of two known methods. Rather, it has been conceptualized to specifically target known challenges in unsupervised model debiasing, successfully leveraging often overlooked properties of widely used tools such as Diffusion Models in a novel way, and our intuition is also supported by the strong empirical performance. \n\nRegarding Section 3.2, introducing DDPMs and CDPMs (accounting for less than a page, including figures) is intended as a background section necessary to provide the basics that are useful for better understanding the main concepts of the work. \n\n\n**W2 (and Q1). More evidence for using diffusion model is recommended: Authors utilized the diffusion model to amplify the bias ... It is recommended to further investigate how the diffusion model behaves when trained on biased dataset. ... Such exploration is essential ...**\n\nWe thank the reviewer for having raised the need of additional analyses to strengthen our contributions. To further highlight that the conditional diffusion model is capturing (and amplifying) the biases of the training set, we manually performed a resampling of the Waterbirds dataset to obtain alternative and less extreme bias ratios ($\\rho = 0.90$, $\\rho = 0.80$, $\\rho=0.70$),  to be used for training our CDPM. From each resulting model, we synthesized 1000 images per class for each $\\rho$, with two independent annotators manually labeling bias-aligned/conflicting samples, thus estimating the degree of correlations $\\rho$ in the generated samples.  We provide this quantitative analysis in the following table.\n\n| Training $\\rho$\t| 0.950\t| 0.900\t| 0.800\t| 0.700\t|\n|----------------|-----------|-----------|-----------|-----------|\n| **Synthetic** ρ    | 0.990     | 0.961     | 0.912     | 0.827     |\n| **ERM**            | 62.60%    | 63.40%    | 64.12%    | 68.84%    |\n| **DDB-I**          | 90.81%    | 86.91%    | 90.19%    | 86.14%    |\n\n\nAs it can be observed, for the original $\\rho=0.95$ the CDPM amplifies the bias, and the resulting synthetic training set presents a $\\rho$ of 0.99. At the same time, the constructed less biased versions of the datasets also result in the diffusion model amplifying the presence of spurious correlations. We thank the reviewer for suggesting this analysis, and we agree that it adds further value to our work and allows for a better understanding of our main hypotheses and results.\n\n\n**W3 (and Q2). Limited discussion on the generated samples: There is limited discussion on the generated bias-aligned samples. ...**\n\nPlease refer to the answer to Reviewer 4iDf, where we provided additional experiments to evaluate the impact of generated image quality over the final debiasing performance.\n\nIn summary, in these experiments, we empirically showed that even with an image resolution of 32x32 pixels or with CDPM trained for less iterations (thus increasing the FID of the generated images), we still obtain compelling debiasing performance, as the generated synthetic images still present dominant bias attributes that are effectively captured by our bias amplifier. \n\nFor the reduced resolution setting (32x32 pixels instead of 64x64),  when employing our Recipe I (BA + G-DRO) on Waterbirds data, we report a WGA of 90.09% (<1% than the original result of 90.81%), thus measuring a negligible impact on the final performance. In reducing the number of CDPM training iterations from 200k down to 70k, we can observe how, with synthetic images presenting a much higher FID, we are still able to reach SOTA competitive results."}]

Author rebuttal:

Meta-review: The paper proposes a novel approach to shortcut learning. The core idea is to generate synthetic bias-aligned examples and use them to train a bias-amplifier model, which subsequently guides the training of the downstream classifier.

Reviewers appreciated the conceptual novelty, compelling empirical results, and the clarity of the paper.

There was disagreement about novelty: Reviewer w3kR argued it is marginal given prior generative methods, while others scored novelty higher.

The AC considers the novelty sufficient for NeurIPS. Given the method’s efficacy, focusing on generating biased synthetic examples appears impactful.

Two reviewers remained negative after the rebuttal. Reviewer EHbf emphasized the need for large-scale experiments or multiple-bias datasets. This concern is valid—especially given the field’s shift toward more realistic shortcut settings—but on its own should not preclude acceptance. Concerns about computational cost were partially addressed during the rebuttal.

A thorough comparison to other generative debiasing methods remains a weakness and was only partially addressed during rebuttal.

Reviewer w3kR also emphasized low novelty. Their other two concerns seem less critical for acceptance. First, the lack of a detailed motivation for using a diffusion-based generator is not decisive here. Second, a deeper discussion of generated images would improve the paper, but is not, by itself, a reason to reject.

In summary, the strengths outweigh the weaknesses raised by the reviewers. I recommend accept.
Decision: Accept (poster)