Enhancing Contrastive Learning for Ordinal Regression via  Ordinal Content Preserved Data Augmentation
Authors: Jiyang Zheng
Yu Yao
Bo Han
Dadong Wang
Tongliang Liu
Venue: ICLR 2024 poster (2024)
Keywords: Contrastive Learning
Ordinal Classification/Regression
Primary area: representation learning for computer vision, audio, language, and other modalities

Abstract: Contrastive learning, while highly effective for a lot of tasks, shows limited improvement in ordinal regression. We find that the limitation comes from the predefined strong data augmentations employed in contrastive learning.  Intuitively, for ordinal regression datasets, the discriminative information (ordinal content information) contained in instances is subtle. The strong augmentations can easily overshadow or diminish this ordinal content information. As a result, when contrastive learning is used to extract common features between weakly and strongly augmented images, the derived features often lack this essential ordinal content, rendering them less useful in training models for ordinal regression. To improve contrastive learning's utility for ordinal regression, we propose a novel augmentation method to replace the predefined strong argumentation based on the principle of minimal change. Our method is designed in a generative manner that can effectively generate images with different styles but contains desired ordinal content information. Extensive experiments validate the effectiveness of our proposed method, which serves as a plug-and-play solution and consistently improves the performance of existing state-of-the-art methods in ordinal regression tasks.

Published as a conference paper at ICLR 2024
ENHANCING CONTRASTIVE LEARNING FOR ORDINAL REGRESSION VIA ORDINAL CONTENT PRESERVED
DATA AUGMENTATION
Jiyang Zheng!2 Yu Yao
Bo Han Dadong Wang²
Tongliang Liu'*
'Sydney AI Center, The University of Sydney PCSIRO, Data61 'Mohamed bin Zayed University of Artificial Intelligence *Camegie Mellon University Hong Kong Baptist University
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ABSTRACT
Contrastive learning, while highly effective for a lot of tasks, shows limited im- provement in ordinal regression. We find that the limitation comes from the prede- fined strong data augmentations employed in contrastive learning. Intuitively, for ordinal regression datasets, the discriminative information (ordinal content infor- mation) contained instances is subtle. Existing strong augmentations can easily overshadow this ordinal content information. As result, when contrastive learn- ing is used to extract common features between weakly and strongly augmented images, the derived features often lack this essential ordinal content, rendering them less useful in training models for ordinal regression. To improve contrastive learning's 's utility for ordinal regression, we propose novel augmentation method to replace the predefined strong argumentation based on the principle of minimal change. Our method is designed in a generative manner that effectively gener- ate images with different styles but contains desired ordinal content information. Extensive experiments validate the effectiveness of our proposed method, which serves as plug-and-plas solution and consistently improves the performance of existing ofte-oofthhe-rr methods for ordinal regression.
INTRODUCTION
Contrastive learning encourages feature consistency between strongly and weakly augmented ver- sions of data (Chen et al., 2020a). This method has demonstrated its effectiveness in both supervised and unsupervised learning contexts (Oord et al., 2018; He et al., 2019; Khosla et al., 2020).
However, the efficacy of contrastive learning diminishes when applied ordinal regression, which aims to predict ordered categories from instances. A primary reason for this limited success is that contrastive learning does not fully account for the unique characteristies of ordinal regression data. Unlike conventional classification data, instances in ordinal regression datasets are inherently characterized by their subtle ordinal content information, which is essential for predicting ordinal labels. This subtlety presents significant challenge for contrastive learning to effectively learn discriminative features, primarily due to the predefined strong data augmentations such as color jittering and color dropping (Chen et al., 2020a; Von Kügelgen al., 2021; Xiao al., 2021). These augmentations can easily overshadow or diminish the vital ordinal content information.
Take ordinal regression task of age estimation may be rich in pixels and colors, age-discriminate features, such as wrinkles or gray hair, are often localized small regions. This localization renders the age-related content information subtle, making it susceptible to being overshadowed by strong augmentations. As shown in Figure 1, the commonly used strong augmentations can distort or even erase these essential features in ordinal regression data, compromising the performance of contrastive learning in such tasks. Similarly, in
example. While images used in this task
"Corresoonding Author

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Published as a conference paper at ICLR 2024
Age: 60+
Losing Ordinal Content Information
Age:
Gaussion Blur
Color Jitter
Heavy Wrinkles
Hoswy-Weinidles
Hossy-Weiinelle
Gray Hair
Gray Hair
Erey-HHii
Figure 1: Ordinal content information in data can be easily distorted by standard augmentations in contrastive learning. As illustrated in the example, the age-related features: wrinkles and hair color are eliminated after Gaussian blurring and color jitter, making the age become unidentifiable.
the task of diabetic retinopathy grading, the parts in the retina images that indicate the severity of the condition, such as mieroaneurysms or hemorrhages (Salz et al., 2016), are often localized and subtle, which can be simply removed by augmentation operations such as Gaussian blurring.
To enhance contrastive learning for ordinal regression, we propose a novel augmentation method as an alternative predefined strong augmentations. Our method is in a generative manner which can effectively preserve ordinal content information. After training with ordinal datasets, it can be leveraged to generate new instances with the same ordinal content information but different styles. To achieve this, we employ a generative model combined with the principle of minimal change.
Specifically, a Generative Adversarial Network (GAN) is employed to generate new instances. How- ever, this generative model does not naturally preserve ordinal content information during the gen- cration process. As indicated by existing work (Hyvarinen et al., 2019; Khemakhem et al., 2020; Von Kügelgen et al., 2021), without any constraints, learned latent factors entangle both ordinal content and non-ordinal information. Such entanglement compromises the controllability of the generative process, making it challenging produce instances with specific ordinal information. To address this, we introduce the minimal change principle (Xie et al., 2022; Kong et al., 2022), which claims that changes to latent factors should have minimal impact the generation of instances.
By leveraging the minimal change, we effectively separate ordinal content factors from non-ordinal ones. In our approach, partition the latent factors into two sets: 2, and Σn The former, å.. serves as "container" for ordinal content information. The latent factors in this set are essential for determining the ordinal category and are generated in alignment with ordinal labels. Minimal change is applied to ε,, thereby limiting its influence during the generation of new instances. As result, once the generative model attains minimal reconstruction error, &, solely captures essential ordinal content information, while E encompasses the remaining non-ordinal information (information that does not contribute to determining ordinal labels). This results the successful disentanglemen of ordinal and non-ordinal information. A more detailed explanation of this disentanglement, along with the generative process introduced by our method, is provided in Section 3.
Our method enhances existing ordinal regression methods by harnessing the benefits of contrastive learning through ordinal content-press preserving augmentations. After learning the latent factors &, and εn with the minimal change principle, our method creates augmented instances that belong specific ordinal label. To achieve it, we fix &, and randomly sample 2, feeding them into our trained generator. This process produces instances with varied styles but consistent ordinal information. These instances can serve as strong augmented views to the natural images. By utilizing these instances with a contrastive learning loss, our method can be seamlessly integrated into existing ordinal regression methods.
The remaining paper structured follows: Section 2 provides an overview of the related works in ordinal regression and contrastive learning. Section 3 details our proposed methodology and its practical implementations. Section includes the experimental validation of our approach on different ordinal regression tasks. Section 5 concludes our paper.
2 RELATED WORKS
Method for Ordinal Regression. Recent advancements often frame ordinal regression as a classi-
fication task (Niu al., 2016; Liu et al., 2017; Beckham & Pal, 2017). Liu et al. (2018b) introduced
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constrained optimization formulation for ordinal regression. This approach minimizes the negative log-likelihood across multiple classes while simultancously preserving the inherent order relation- ship between instances. Diaz& & Marathe (2019) leveraged the natural ordinal relationships between targets, imparting them as prior knowledge to the model through soft labels. Liu et al. (2019) addressed the task from probabílistic modeling perspective, where Gaussian Process model is attached to the output layer of the deep neural network to model uncertainty. Li et al. (2021) pro- posed framework that employs probabílistic embeddings to model data uncertainty. Their method enforces a constraint between the learned embedding distributions and pre-defined ordinal distri- butions, ensuring that the learned embedding space remains ordered. Shin al. (2022) proposed regression-based rank estimation algorithm that learns to model the order relationship between instances. Cheng et al. (2023) propose a data fusion approach to address the class-imbalance issue in ordinal regression datasets. While most of the previous studies primarily focused on aligning the model's final predictions with the target, our approach emphasizes the importance of preserving ordinal content information when augmenting ordinal regression data. Our method is orthogonal to end-to-end trainable ordinal regression model, which can serve as a plug-and-play solution to improve the performance of existing state-of-the-an ordinal regression frameworks.
Contrastive Learning and Data Augmentations. Contrastive learning (CL) extracts discrimina- tive information from data by organizing samples into similar and dissimilar pairs. It amplifies the similarity of similar pairs and increases the difference between dissimilar pairs in the feature space. In self- self-supervised setting (He et al., 2019; Chen et al., 2020a), similar pairs are generated through a data augmentation module. Given reference image, this module introduces modifications such as random scaling. cropping, color jittering, blurring, and flipping to generate new perspectives of the image. The original image and its augmented versions constitute similar pair. In contrast, other images in the batch are treated as dissimilar samples, forming dissimilar pairs. Khosla et al. (2020) expanded CL to supervised setting. In addition to the augmented versions of the image, samples from the same class also become part of a similar pair. Zha et al. (2022) introduced a supervised CL framework for regression tasks, ensuring that the order of representations in the feature space corre- sponds to their target values. The results demonstrate that existing regression methods consistently benefit from a CL module for extracting discriminative features from data.
Furthermore, intensive data augmentations have been found crucial for the success of the contrastive learning framework across all settings (Chen et al., 2020a;b; Khosla et al., 2020; Li et al., 2023; 2022a; Huang et al., 2021; Zheng et al., 2022). However, such aggressive augmentations can com- promise an image's 's content. Xiao et al. (2021) addressed this breach of the invariance assumption (i.e.. data augmentations altering the data's semantic information) by decomposing a compound series of augmentations into individual operations and creating distinct heads for each single aug- mentation. This method is effective when data is sensitive to a few specific augmentations within the full augmentation sequence but maintains its invariance assumption with others. Given that content information in ordinal data is particularly susceptible, many augmentation techniques can potentially distort an image's s semantics, thereby limiting the method's 's efficacy. Compared to their approach, method does not depend on any predetermined augmentation method. Instead, we guide the model to discern which aspects of the augmented data should be preserved as content variables and which can be modified to introduce diversity as style variables.
ORDINAL CONTENT PRESERVING CONTRASTIVE LEARNING (OCP-CL)
In this section, we introduce our Ordinal Content Preserving Contrastive Learning (OCP-CL) frame- work. Specificallyy, to improve the utility of contrastive learning for ordinal regression, we propose a novel ordinal content-presse preserving augmentation method that replaces the predefined strong aug- mentations in contrastive learning framework. First, we explain the generative process of ordinal regression data, which serves as the foundational understanding of our proposed generative model. Then, we introduce our approach for disentangling ordinal content and non-ordinal factors via min- imal change, and detail the implementation of the generative model. Next, we describe the process of generating content- content-preserving preserving data augmentations through interventions on non-ordinal latent factors. Finally, we present the contrastive learning formulation with our generated augmentations from the original instances. The contrastive learning objective can be integrated into any existing end-to-end trainable deep ordinal regression methods to form a joint objective.
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Data Generative Process. We first explain the causal data generative process (Glymour & Zhang, 2019; Yao et al., 2023) as illustrated Figure 2. The graph outlines the generative process for ordinal regression data, segregating latent factors into different functional groups based on their relationships to the observed variables.
Specifically, 2, denotes set of invariant ordinal factors that cap- ture all ordinal content relevant features across different ordinal cat- egories. For example, in age estimation, žy encompasses compre- hensive collection of age-specific attributes such as the severity of wrinkles or variations in skin texture across age groups. The ordinal label serves as constraining variable, ensuring that 2o selectively retains only those features from 2, that pertain its corresponding ordinal category. Similarly, 2n denotes the set of non-ordinal, style- related factors. Together, 2o and 2n collaboratively generate æ; the observed data instance. Our primary objective for the generative model to disentangle the learned latent factors into & and &. in alignment with the proposed SCM. In this setup, & is approxi- mated to only contain ordinal content information that determines the ordinal category, whereas ε, holds styling information. Gen- erating content-preserving samples hinges on accurately recovering the true joint distribution of image and class, denoted as P(X, Y). Based on our proposed generative process. the causal factorization of the joint distribution ((X, Y. Zo Z, Zn) is:
Y
Zv
Zo
Zn
X
Figure 2: The data genera- tive process employed by our method. The shaded vari- ables are observable and the unshaded variables are latent.
P(X,Y Zo Zv Zn) P(Y) Z.) Zn).
Our method is designed to fulfill the this generative process by modelling each probability in Eq. 1.
3.1 PHILOSOPHY OF DISENTANGLING ORDINAL CONTENT FACTORS.
The principle of minimal change is pivotal in our generative process, aiding in the effective disen- tanglement of non- ordiaal factors from ordinal content factors. To underscore the importance of this principle, we first discuss the limitations of relying solely on ordinal labels y. Subsequently, we explore scenarios that incorporate the minimal change principle. We consider generative model (Zhou al., 2023; Yao et al., 2021) that aligns with the generative process depicted in Figure and infers the factors 2, E. and ε, After the learning phase, in which the reconstruction error is minimized, our goal is to align & with non-ordinal factors zni align & with ordinal content factors zo: and align 2, with the set of invariant ordinal factors zy. Note that 2n. 2o and zy are the true latent factors in the data generative process.
If we rely solely ordinal labels y. the generation of &. is influenced by both y and 2. This relationship can be mathematically expressed as: & = (y, 2,) + E. In essence, this generation mechanism allows é, assimilate information from both y and E,. Given the generative model's typical assumption that both &n and é, follow a standard Gaussian distribution, non-ordinal infor- mation can feasibly reside either &. or & These factors are fundamentally similar, and their differences are purely notational. Hence, the choice of where to store non-ordinal information does influence the reconstruction error. As a result, å. being influenced by both y and λ,, inevitably contains non-ordinal information. Thus, the disentanglement can not be achieved solely with y.
To address this, we apply the minimal change principle in the disentanglement process. The minimal change principle serves as constraint on image generation, ensuring that the influence of certain factors during instance generation remains minimal. Specifically, when applied to 2,, this principle limits the factor's s influence. Consequently, the information within 2, remains minimal and focused. By introducing an additional constraint on the generative function 9, we ensure that & inherently carries information related to Y. Assuming the reconstruction error is minimized, this suggests that ordinal content information is primarily included within the latent factors 2. As & is generated independently of the ordinal label Y, it will not contain ordinal content information. Therefore, all ordinal content information becomes localized within &, Furthermore, by minimizing the influence of E., it becomes exclusively representative of ordinal content information.
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Generated Image
y
Ordinal Label
Generator
Discriminator
Mask
f
Ordinal
Z Z n
Z
o
G
D
Non-Ordinal
Disentangled Latent Factors
Real
Image
Architecture of Our Generative Model. The class label y is leveraged for both disentan- gling latent factors and enforcing minimal change. An ordinal head is appended to the discriminator preserve the ordinal distribution of generated samples in relation their class.
Figure 3:
3.2 ORDINAL CONTENT AND NON-ORDINAL INFORMATION DISENTANGLEMENT VIA MINIMAL CHANGE
Our primary objective is to achieve minimal change in the generation process, specifically by con- straining the influence of ordinal content factors 2.. This is realized by limiting the number of these factors. To this end, we introduce mask operation, consistent with our data generative process. The essence of this mask is regulate the quantity of ordinal content factors. sparser mask trans- lates to fewer ordinal content factors. To promote this sparsity, we impose an L1 loss the mask, represented as s0 M| Let's define our latent factors = [ 2n] and onn == [ 2,]. The mask operation is then given by:
(2) In the above equation, encapsulates both the invariant ordinal content factors Ê, and the non- ordinal factors 2n The label distribution P(Y) is derived empirically by counting the occurrences of each label within the dataset and then normalizing these counts to form probability distribution. We utilize the Deep Sigmoidal Flow (Huang et al., 2018) for the function fy. It is a type of nor- malization flow characterized by the use of small neural networks with sigmoid units. These units introduce inflection points in the transformation function, enabling the modeling of complex proba- bility distributions. Within our context, it serves as component-wise transformation function that transforms 2.n component-wiss manner. This function, when applied, acts as a label influence. Although it impacts all elements in é, each element undergoes independent transformation. The mask operation on 2 rejects certain transformed elements. By adding 2 back, we ensure that certain elements remain uninfluenced by the label Y, effectively distinguishing them as non-ordinal factors.
on = 2 M f2(), 8~ 80,1,, y P(Y).
It's worth noting that for elements unaffected by the mask but influenced by y. the addition of elements from & is inconsequential. Given that & is sampled from a nigh-dimensional Gaussian distribution, adding it to these elements is akin to introducing random Gaussian noise, ensuring our method remains consistent with the proposed data generative process.
To make latent factors, we employ a Generative Adversarial Network (GAN) model (Mirza & Osin- dero, 2014). The architecture of this model is illustrated in Figure 3. The GAN comprises two main components: a generator Go and discriminator Da each parameterized by their respective learn- able parameters and c. The generator's 's role to craft realistic instances, while the discriminator endeavors to differentiate between genuine and generated instances. The GAN loss, vital for image reconstruction, formally articulated
Gyan (loog( E[log(1 De(Go(Eun))))
(3)
In the given formulation, (x) represents the discriminator's estimated probability that the in- stance z is sampled from the real data distribution. The generator, denoted by Gg. aims to produce instances that the discriminator D& perceives real, maximizing the likelihood of them being clas- sified as genuine. Conversely, the discriminator D, endeavors to distinguish real instances from those generated by Ga, classifying them accurately either real fake.
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The objective function for disentangling ordinal content and non-ordinal information is combined with the GAN loss and the sparsity loss, i.e.,
arg min Caug = arg min Ggan + X Cap. (d.iifu.M)
(4)
(d.
where À is the coefficient control the contribution of the sparsity loss the overall objective.
3.3 CONTENT-PRESERNEE AUGMENTATION FOR ORDINAL REGRESSION
Our method is crafted to complement existing ordinal regression techniques, leveraging the strengths of contrastive learning. After training, we could have generator Gj- To generate instance x' corresponding to specific ordinal label, we employ Eq. 2. For instance, generating example for the ordinal label = can be achieved by:
z GG((un)) son 2 M O f1(2), ~~ NO, Y=1.
(5)
Specifically, we start by sampling 8mm. By setting the ordinal label Y = 1. we utilize (2) to generate łon specific the ordinal label Y 1. By sampling different e.n values and maintaining the ordinal label Y constant, we can generate diverse instances that, while exhibiting stylistic variations, consistently belong to the label Y.
To integrate with existing ordinal regression methods, x' can be employed the strongly augmented data. We then incorporate the supervised contrastive loss (Khosla et al., 2020) as a regularization term for the prevailing method. This loss emphasizes intra-class similarities while concurrently maximizing inter-class disparities. Let's define X as the feature space of x. We introduce he as model with learnable parameters vb, which is used by the ordinal regression method. For given instance v. (x) = z outputs the latent representation z used for ordinal regression. The contrastive loss on ho is defined as:
exp (av() hw(x)//T) log Σnen exp (h (2:)) h (xb)/T)
C
Ccom
Σ
((h))))))
EE
ief
(6)
In the equation above, denotes the set of sample indices in the batch. For each instance æ; belong- ing to y. its strongly augmented counterpart z is generated using our method by setting the ordinal label Y y during the generation process.
EXPERIMENTS
In this section, we evaluate our method across three real-world applications within the domain of ordinal regression: age estimation, diabetic retinopathy rating, and weather condition prediction. Due to space constraints, we include qualitative analyses of our generative model in Appendix D.
Baselines. We employ five state-of-the-art deep learning- based ordinal regression methods as our baselines. OR-CNN (Niu et al., 2016) utilizes a series of binary classifiers and optimizes the model through the one-hot encoding of labels. CNNPOR (Liu et al., 2018b) 8b) reduces the multi-class negative log lilceliodoood while concurrently maintaining the intrinsic ordinal relationship among in- stances. SORD (Diaz& & Marathe, 2019) employs soft labeling strategy during training. POE (Li et al., 2021) captures data uncertainty via probabilistic embeddings. MWR (Shin et al., 2022) lever- ages auxiliary set of reference images model ordinal relationships. All these models are end- to-end trainable. We seamlessly integrate our contrastive learning objective into their original loss formulations, augmented with ordinal content- presertinn data transformations.
Experimental Settings. For the generative model, we use StyleGAN2 (Karras et al., 2020) as the base model, A is set to le-4 across all settings. For all ordinal regression methods, we use VGG16 (Simonyan & Zisserman, 2014) the base deep neural network architecture, with Im- ageNet (Deng et al., 2009) pre-trained weight for initialization. We employ an embedding layer before the final output layer in the model to extract feature embeddings. The dimension of feature embedding is set to 128. The ratio contrastive loss consistently set to le-4 for OR-CNN, CN- NPOR and POE, and le-5 for SORE and MWR. For the three datasets, the input images resized

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wo OCP-CL
w/ OCP-C-L
Adience
Accuracy (1)
MAE (4)
Accuracy (1)
MAE (J)
OR-CNN (Niu et al., 2016)
54,6 5.5
0.60 + 0.09
57.1 (+2.))
0.56 + 0.06 (+0.04)
CNNPOR (Liu et al., 201
55.1 = 6.0
0.60 2 0.08
57.7 = (+2.6)
0.55 - 0.07 (+0.05)
SORD (Diaz & Marathe, 2019)
57.8
4.9
0.53 0.06
59.9 4: (+2.))
0.49 :: 0.06 (+0.04)
POE (Li et al., 2021)
60.5
- 4.8
0.47 + 0.08
63.7 - (43.2)
0.43 - 0.07 (+0.04)
MWR (Shin et al., 2022)
62.6
5.0
0.45 + 0.08
63.6 +
4.7 (+1.0)
0.43 + 0.07 (+0.02)
w/o OCP-CL
W/C-PCCL
Diabetic Retinopathy
Accuracy (1)
MAE (4)
Accuracy (1)
MAE (1)
OR-CNN (Niu 2016)
71.9 + 1.3
0.42 + 0.01
72.8 0.7 (+0.9)
0.41 + 0.00 (+0.01)
CNNPOR (Liu al., 201 8b)
71.3 -
1.1
0.42
+ 0.02
72.6 + (+1.3)
0.41 = 0.01 (+0.01)
SORD (Diaz & Marathe, 2019)
69.1 + 1.0
0.45 4 0.01
69.9 + 1.1 (+0.8)
0.44 - 0.01 (+0.01)
POE (Li et al., 2021)
73.6
1.0
0.40
+ 0.01
74.8 = 0.8 (+1.2)
0.38 = 0.00 (+0.02)
MWR (Shin al., 2022)
74.5
1.1
0.38
0.02
75.1 + 1.1 (40.6)
0.37 : 0.01 (40.07)
w/o OCP-CL.
WCP-CCL
SkyFinder
Accuracy (1)
MAE (L)
Accuracy (1)
MAE ())
OR-CNN (Niu et al., 2016)
60.3 + 2.1
0.48 + 0.03
2.1+223000 2.
0.46 + 0.04 (+0.02)
CNNPOR (Liu et al., 8b)
57.6 1.6
0.52 + 0.03
59.7 = (+2.))
0.49 + 0.03 (+0.03)
SORD (Diaz Marathe, 2019)
58.2
1.9
0.06
(+2.))
0.48 0.04 (+0.03)
POE (Li et al., 2021)
61.9
1.7
0.46 + 0.05
64.1 = (+2.2)
0.42 = 0.05 (+0.4)
MWR (Shin et al,, 2022)
62.4 =
1.8
0.45
+ 0.05
63.2 +
1.9 (+0.8)
0.44 + 0.06 (+0.01)
Table 1: Accuracy (%) and MAE comparison on Adience dataset (Eidinger et al., 2014), Diabetic Retinopathy dataset (Liu et al., 201 8a) and SkyFinder dataset (Mihail et al., 2016).
into 256 256 and center cropped into a sub-region of 224 224. Adam (Kingma & Ba, 2014) optimizer is used for all baseline methods, with a base learning rate of le-4. We uniformly train all baseline models for 200 epochs with batch size of 256 for all baselines except MWR. For MWR, the batch size is set to 128 due to memory constraints. We report the results via the accuracy and mean absolute error (MAF) metries. For the other parameters in the baselines, we adhere to the orig- inal settings designed in the papers unless specified in our experimental settings. While we employ our techniques for strong augmentations, weak augmentations are achieved solely through resizing, center cropping, and normalizing the original instances. No other augmentation methods are applied to the data. All experiments are conducted in on two 48GB NVIDIA RTX A6000 GPUs.
4.1 AGE ESTIMATION
Dataset.
Age estimation is the task of predicting age groups based on facial images. The Adience dataset (Fidinger et al., 2014) comprises 26,580 photos from Flickr, featuring 2,284 subjects. These photos are annotated across eight age groups: 0-2, 4-6, 8-13, 15-20, 25-32, 38-43, 48-53, and over 60 years. The dataset adheres to a standard five-fold, subject-exclusive tross-validation protocol, as widely utilized in previous studies (Rothe et al., 2018: Shen al., 2018; Li et al., 2019; 2021). The generative model is trained in accordance with the training fold of this protocol. For each instance in the dataset, we generate augmented views using the generative model, the augmented views and the original instances are jointly trained by the models.
Results. We present the experimental results in Table (Top). Employing our proposed OCP-CL method, OR-CNN experiences a 4.58% boost in accuracy and a 6.67% reduction in MAE. CN- MAE. SORD's
NPOR benefits from a 4.72% increase in accuracy and an 8.33% improvement performance is uplifted by 3.63% in accuracy and 7.55% in MAE. POE sees the largest accuracy improvement of 5.29% and an MAE reduction of 8.51% %. Lastly, MWR has a modest 1.6% increase in accuracy and a 4.44% decrease in MAE. This consistent improvement across multiple ordinal regression methods validate the efficacy our OCP-CL approach for the task of age estimation. Additionally, we visualise the generative augmentations in Figure 1. We observe that the augmen- tations have preserved the ordinal content information for their respective age groups, capturing details such the sparse eyebrows of children, silky skin texture of young adults, and the pro- nounced wrinkles of seniors, thereby allowing the ordinal regression methods and the contrastive learning framework to effectively learn the critical ordinal content information.
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Age Group: 4-6
Age Group: 25-32
Age Group: 60+
Figure 4: Generated augmentations for the age estimate task, the collections corresponds to the age group of (4-6), (25-32), and 60+ respectively.
4.2 DIABETIC RETINOPATHY RATING
The Diabetic Retinopathy dataset' is utilized for predicting the severity stages of Diabetic Retinopathy based on high-resolution RGB retina images. The dataset consists of 35, 126 individual instances, each annotated into one of five ordinal categories representing increasing levels of severity (i.e., No DR, Mild, Moderate, Severe, and Proliferative DR). The dataset is partitioned into training, validation, and testing sets, which constitute 80%. 5%, and 15% of the total dataset, respectively. The dataset contains 25,810, 2443, 5292, 873 and 708 images for each category, respectively. Ac- count for the imbalances between adjacent categories, we generate augmented views dynamically depending on the ground truth label, which mitigates the class imbalance issue. Specifically, the number of augmentations for instances from each increasing level of severity is set as [1, 3, 2, 5, 5].
Dataset.
Results. In Table 1 (Middle), evaluate the performance of various ordinal regression methods on the Diabetic Retinopathy dataset, with and without the incorporation of our proposed OCP-CL (Ordinal Content-Preservvng Preserving Contrastive Learning) module. Remarkably, all the compared meth- ods exhibit improvement both accuracy and MAE upon integration with the OCP-CL module. Practicularly, the incorporation of the OCP-CL module results in accuracy improvements of 1.25%, 1.82%, 1.16%. 1.63%, and 0.81% for OR-CNN, CNNPOR, SORD, POE, and MWR, respectively. Concurrently, the MAE reduces by 2.38%, 2.38%, 2.22%, 5.00%, and 2.63%, respectively. These results collectively indicate that the introduction of the OCP-CL module consistently enhances the performance across diverse set of ordinal regression models. This validates the generalizability and efficacy of our proposed OCP-CL approach in boosting performance for ordinal regression tasks.
4.3 WEATHER CONDITION PREDICTION
The SkyFinder Dataset (Mihail et al., 2016) comprises 94,804 labeled outdoor images, sourced from static webcams affiliated with the Archive of Many Outdoor Scenes (AMOS). These images encapsulate broad spectrum of weather and lighting conditions. A specialized subset of 62,988 images, specifically featuring the weather conditions of Clear, Partly Cloudy, and Mostly Cloudy, has been curated to create weather prediction dataset. This subset is further partitioned into training, validation, and testing sets, constituting 80%, 5%, and 15% of the dataset, respectively. For each instance in the dataset, we generate augmented views using the generative model, the augmented views and the original instances are jointly trained by the models.
Dataset.
Results. Table (Bottom) presents the results our experiments, highlighting the performance improvements achieved by all bascline models upon the incorporation of the contrastive module. Specifically, the accuracy improvements for the baselines are 2.9%, 3.5%, 3.8%, 3.4%, and 1.1% for OR-CNN, CNNPOR, SORD, POE, and MWR, respectively. Similarly, the improvements in MAE for the baselines are 4.2%, 5.8%,5 5.9%, 8.7%, and 2.2%, respectively. With an average improvement of 2.94% in accuracy and 5. 5.42% in MAE, these results demonstrate the efficacy of our method in hancing the performance of deep-learning- based ordinal regression models on the weather condition estimation task.
4.4 ANALYSIS
Transfer Learning. In this section, evaluate the transfer learning performance of trastive learning approach. Initially, we pre-train the encoder using a contrastive learning objective
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Minimal Change in Image Generation. We study the effect of minimal change on ordinal data generation by manipulating set to Published as a conference paper at ICLR 2024
SupMoCo (He et al., 2019)
SupCon (Khosla et al., 2020)
S-LooC (Niao et al., 2021)
SupCReg (Zha et al., 2022)
OCP-CL (Ours)
Dataset
Accuracy
MAE
Accuracy
MAE
Accuracy
MAE
Accuracy
MAE
Accuracy (1)
MAE (J)
0.52
60.5
0.55
0.52
62.7
0.53
65.2
0.50
Adience
51.9
0.65
51.7
0.67
52.4
0.64
51.2
0.63
53.1
0.61
SkyFinder 56.1
0.55
53.9
0.59
55.6
0.56
55.1
0.55
57.5
0.52
Table 2: Linear evaluation on supervised contrastive learning frameworks. Accuracy (%) and MAE are reported for various ordinal datasets including Diabetic Retinopathy dataset, Adience (Levi & Hassner, 2015) and SkyFinder dataset (Mihail et al., 2016).
for feature extraction. Following this, freeze the trained encoder and employ the extracted fea- tures as input to a single-layer MIP predictor, which is then fine-tuned on the training data. We assess the efficacy of our approach against recent state-of-the-art supervised contrastive learning frameworks across three different tasks. To mitigate performance degradation due to parameter set- tings, we dynamically adopt the recommended configurations from the original papers. However, for SupCon (Khosla et al., 2020), batch size of 1024 is unfeasible for image instances of size 224 by 224. To ensure convergence, we reduced the image size to 64 by 64 and the batch size to 512. The results are presented in Table 2. Notably, our method significantly outperforms all ex- data
isting approaches in the task of ordinal regression. The benefits of ordinal content-preservin augmentation become evident when benchmarked against SupMoCo (He al., 2019). Specifically, we adopt the SupMoCo framework as the baseline contrastive learning framework and integrate our augmentation strategy by replacing the original data augmentation modules. This aids in evaluating transfer learning performance. The contrastive loss formulation in SupMoCo aligns with our Eq. 6, and an additional momentum encoder is incorporated to ensure training convergence. The MoCo strategy is not employed in other experiments.
maak 0
the mask hyperparameter. When this hyperparameter zero, sparsity is not enforced, effectively removing the minimal change constraint from the generative process. As illustrated in Figure 5 (Amask = 0 + no minimal change), the absence of mini- mal change leads to the inclusion of non-ordinal features, such as hair, which do not contribute identifying infant age groups and should be considered non-ordinal factors. However, in the case where minimal change is not applied, these features are learned as ordinal content factors and appear in all generated images, thereby demonstrating poor disentanglement performance. By enforcing minimal change, these non-ordinal elements are sup- pressed, enhancing the overall quality of the generated instances.
A mask 0.1
Figure 5: Influence of minimal change in image generation.
CONCLUSION
In this paper, we address the open challenge of applying contrastive learning to ordinal regression tasks. We find that the strong data augmentations in the contrastive learning frameworks often dimin- ish the intrinsic discriminative semantic information associated with ordinal labels. Consequently, when contrastive learning is used to identify invariant features between weakly and strongly aug- mented views, the extracted features frequently lack the essential ordinal content information. To mitigate this issue, we introduce novel augmentation method grounded in the principle of minimal change. This generative approach ensures that the images retain the essential ordinal content infor- mation during the data augmentation process. As result, our method enhances the applicability of contrastive learning to ordinal regression tasks. Extensive experiments validate the efficacy of this approach in improving the performance of existing ordinal regression models. This work not only broadens the scope contrastive learning in ordinal regression but also provides valuable insights for future research aimed at preserving crucial task-specific information during data augmentation.

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6 ACKNOWLEDGEMENT
The authors would like to thank Muyang Li and Xiaobo Xia for their valuable feedback through- out the project. Jiyang Zheng is supported by the CSIRO Next Generation Graduates and AI for Missions PhD program. Tongliang Liu is partially supported by the following Australian Research Council projects: FT220100318, DP220102121, LP220100527, LP220200949, and ICT90100031. BH was supported by the NSFC General Program No. 62376235, Guangdong Basic and Applied Basic Research Foundation Nos. 2022A151501 1652, 2024A 515111339 HKBU Faculty Niche Re-
search Areas No. RC-ENRA-IG/2--2/SCIIN and HKBU CSD Departmental Incentive Scheme.
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APPENDIX
A ABLATION ANALYSIS
Number of Augmented Instances. We conduct an experiment to assess the sensitivity of the models to the number of augmented instances. Specifically, adjust the number of views from the set [1,3,5,10] and "dynamic", and exam the performance on POE w/ OCP-CL. As illustrated in Figure 6, the models' performance remains relatively stable when the number of augmented instances ranges between and 5. However, over-suppiemnnting the data with augmented instances can lead degradation model performance. Interestingly, we find that a dynamic number of augmentations depending the class could benefit the models. This is particularly relevant for ordinal regression datasets that suffer from class imbalance. Specifically, by increasing the number of instances in underrepresented classes, observe improvement in the overall performance of the methods.
Adience
DR
Skyfinder
Adience
Skyfiiner
74
0.48
72
70
Accuracy
MAV
0.44
66
64-
0.40
62
3
10
Dynamic
10
Dynamic
Augmented
Number Augmented Views
Figure 6: Ablation Study the Number of Augmented Views.
Adience
DR
SkyFinder
Adience
DR
SkyFiidder
74
72
0.46
70
Accuracy
88
66
0.42
0.40
0.38
0.001
0.01
0.1
0.5
0.001
0.01
0.1
0.5
A
Figure 7: Sensitivity Analysis on \ ratio.
Sensitivity Analysis ' We conduct experiments to assess the impact of the mask sparsity ratio, denoted as A₁ on the performance of the ordinal regression model across three downstream tasks. For this purpose, utilize POE w/ OCP-CL examine sensitivity to changes in A We
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test five different sparsity ratios within the range of 1e-3 and 1. The results, presented in Figure 7, indicate that the ordinal regression model achieves optimal performance when Y1 is set to 0.1 for all downstream tasks, and its performance decreases linearly with increases in the ratio beyond 0.1.
B ADDITIONAL RELATED WORKS
In this section, we provide additional background discussions relevant our work. Specifically, we discuss recent advancements in Generative Data Augmentation, Disentangled Representation Learning, and Nonlinear ICA and explore their relationship with our research.
Generative Models for Data Augmentations. Instead of generating data augmentations using predefined transformations. Generative Data Augmentation (GDA) employs an alternative approach that leverages Deep Latent Variable Models (DLVMs) to generate new synthetic views from existing samples, based on conditional generative processes. Antoniou et al. (2017) and Tran al. (2017) propose the use of Generative Adversarial Networks (GANs) (Goodfellow et al., 2014; Xia et al., 2022) to create a broader set of augmented data. More recently, Diffusion Models (DMs) (Sohl- Dickstein et al., 2015) have been utilized to alter high-level semantic attributes, thereby addressing the problem of lack of diversity along key semantic axes in data augmentation (Trabucco et al., 2023). While these approaches can generate impressive results that appear both realistic and novel, most of them are not guaranteed to maintain the invariance of the original data. Our proposed method fulfills the need for controllable generative data augmentation, offering more trustworthy GDA approach.
Disentangled Representation Learning. The objective of Disentangled Representation Learning (DRL) is to construct model proficient in recognizing and isolating the latent factors concealed within observable data (Wang al.. 2022). This isolation into semantically meaningful factors enhances the model's ability to produce interpretable data representations, thereby simulating the cognitive processes humans employ in understanding objects or relationships. In the context of gen- erative modeling, Higgins et al. (2017) introduce 8-penalty coefficient for the KL divergence term in the evidence lower bound of a Variational Autoencoder (VAE) (Kingma & Welling, 2013; Li et al., 2022b; Huang al., 2022; Hong et al., 2024; Lin et al., 2023) to balance latent channel capacity and independence constraints with reconstruction accuracy. Subsequently, various modifications to VAE have been introduced to improve its capability for disentanglement (Chen et al., 2018; Kumar al., 2017). These include the incorporation of either implicit or explicit inductive biases as well as the utilization of diverse regularization techniques. On the other hand, InfoGAN (Chen et al., 2016) was the first to address the problem of disentangling latent factors in Generative Adversarial Networks, introducing an extra variational regularization of mutual information. Lin et al. (2019) introduced InfoGAN-CR, an unsupervised extension of InfoGAN that includes contrastive regularizer to infer latent dimensions. Zhu et al. (2021) present PS-SC GAN, which builds upon InfoGAN and features a Spatial Constriction (SC) strategy to extract significant areas influenced by each latent dimension, along with a Perceptual Simplicity (PS) approach to make the latent factors more unambiguous. Wei et al. (2021) propose a method known as Orthogonal Jacobian Regularization (OroJaR) aimed at enhancing disentanglement in generative models. OroJaR uses the Jacobian matrix to examine how output alterations correspond to changes in input variables, specifically the latent dimensions. Our methods are parallel to GAN-based DRL methods, wherein we disentangle the latent factors by introducing the principle of minimal change.
Nonlinear ICA. Nonlinear independent component analysis (ICA) theoretically addresses the problem of disentangling latent factors when a nonlinear invertible transformation function exists, mapping independent samples to the latent space Hyvarinen & Morioka (2016; 2017). Recent de- velopments Locatello et al. (2020); Zimmermann et al. (2021); Xie et al. (2022); Kong et al. (2022) indicate that, within a conditional generative process, the true latent factors might become identifi- able when auxiliary information is provided. Khemakhem et al. (2020) demonstrate that the joint data and latent space distributions can be recovered, up to simple transformation in the latent space, provided the generative process conditions on a variable observed alongside the data. Von Kügelgen et al. (2021) employ two views of the same image to disentangle the latent factors into ordinal con- tent and non-ordinal components, with only the content component associating with the image's 's semantics. Our generative model leverages Nonlinear ICA theories to theoretically justify the disen-
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tanglement of latent factors. By manipulating the ordinal content variable, while keeping the ordinal content factors consistent, our model can generate augmented views that preserve ordinal content.
C INTUITION OF WHY AUGMENTING NON-ORDINAL FACTORS
Here, we provide further discussion on why augmenting non-ordinal factors in images can benefit the training of ordinal regression/clasffcation models. In general, data augmentation aims to modify the styling factors in original examples that are not related to the predictive objectives of downstream tasks (Von Kügelgen et al., 2021).
In computer vision tasks, by changing the styles in images, we add more variety to the training data. This helps the model not to focus too much on the specific styles it sees in the training images. It teaches the model to recognize objects or features images, no matter how the style of the image changes. This is important because in the real world, images can come in many different styles. So, adding style changes in training helps the model perform well on all kinds of images
In the context of ordinal regression, style information is referred to as non-ordinal information, gov- erned by underlying non-ordinal factors. Our method also aims to enrich style diversity by altering non-ordinal information. This is achieved by randomly sampling non-ordinal factors while main- taining the ordinal content factors, then generating examples based on these factors. By preserving the ordinal content factors, can change the image's style while keeping its ordinal content un- changed, thereby generating synthetic (counterfactual) images not seen in the training data. This approach enables neural networks to access more samples with diverse styles, thereby improving the generalization capabilities of ordinal regression models for unseen samples. As demonstrated in Figure 7, by randomly sampling non-ordinal factors while maintaining the ordinal content factors, we can alter various aspects of the image's 's style, such as people's 's dressing, background, and camera angles, etc., ensuring the ordinal content remains unchanged.
It is also important to emphasize two major advantages of our data augmentation methods: Firstly, existing image augmentation strategies do not guarantee the preservation of ordinal information dur- ing the augmentation process. For example, color jittering can change an image's ' color, potentially altering white hair to yellow, which could obscure the age of the person in the image. Secondly, our proposed data augmentation method is general. Our approach can be broadly applied to tomatically infer ordinal content from other types of information and generate new examples with guarantees. While primarily tested image data, our method's framework should be adaptable to non-image data. This adaptability is not achievable with traditional data augmentation methods, which mainly focus image data. For instance, applying rotations non-image data is not feasible.
D VISUALISATION OF DATA AUGMENTATION
We provide additional visualizations of the augmentation results. As shown in Figure 8, we con- ducted experiments on three ordinal regression datasets. Our findings indicate that our augmentation method effectively retains age-related and weather-related features, while simultaneously introduc- ing significant stylistic variations. In the case of diabetic retinopathy instances, the differences between the original and augmented views are subtle. Without domain-specific knowledge, it is challenging to conclusively determine whether the ordinal content has been preserved. Figure 9 demonstrates the generative results for altering the ordinal factors. For each instance, we fix the non-ordinal factors and replace the ordinal factors with age-specific ordinal factors (i.e., represent- ing different age groups). The age-specific ordinal factor extracted from training images of the corresponding age group. By visualizing the results, we can observe that the age of the individ- uals has changed following augmentation, while the styling information from non-ordinal factors remains similar. This effectively illustrates the efficacy of our method in disentangling ordinal and non-ordinal factors. We also present image generation results from a conventional GAN (Karras et al., 2020) in Figure 10. It is important to note advantage of our method over conventional GANs: the inability of conventional GANs to disentangle ordinal factors from non-ordinal factors. This means they cannot guarantee the preservation of image's semantic information. Addition- ally, our model focuses more on fine- aeee-granedd details when constructing novel samples. This evi- dent in the detailed modeling of rge-related components in facial images. While conventional GANs
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achieve high generative quality, they sometimes fail accurately represent certain age-related features in the images, such as generate hair for infants and child face for seniors.
Instancex
Augmeniations
Instonces
Augmentations
Instances
Augmeniations
Figure 8: Generated augmentations by augmenting the non-ordinal factors ξn
Instances
Augmentations
Figure Generated augmentations by augmenting the ordinal factors &o with age-specific factors.
Conventional GAN
Ours
Figure 10: Unconditional image generation results of conventional GAN (the first Row) and Our method (the second Row).
17


Reviews:
[{"review_id": "YVAP5uIrcD", "reviewer": "Reviewer_SPqB", "summary": "The authors aim to enhance contrastive learning methods for ordinal regression tasks. They propose to disentangle ordinal content from non-ordinal content in latent factors and focus on augmenting non-ordinal information. Experiments on 3 public datasets are conducted to demonstrate the effectiveness of the proposed method.", "questions": "Please see the weaknesses part. I would be inclined to increase my rating if the questions in the weaknesses part were well addressed and explained.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "1. The proposed method can be easily integrated into existing methods.\n\n2. The experiment results look promising.", "weaknesses": "**Majors:**\n\n1. If one latent feature (non-ordinal content) does not contribute much to an ordinal regression downstream task, then how much help could its augmentation provide? I would like to see some analysis about it.\n\n2. The authors use an example in Figure 1 to show that \"the commonly used strong augmentations can distort or even erase these essential features in ordinal regression data.\" Can you try some strong augmentation methods to demonstrate this claim in Tables 1, 2, and 3?\n\n3. What about the performance of OCP-CL when the mask sparsity ($\\lambda_1$) changes?\n\n4. For results in Tables 1-4 and Figure 6, are they average of multiple runs? What about the standard deviations?\n\n**Minors:**\n\n5. The proposed OCP-CL is somehow similar to feature selection for content disentangling. Can feature selection methods be applied to learn ordinal and non-ordinal content?\n\n6. How did you get the numbers in Table 5? By setting a threshold for $M$?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lJvpTH5BB9", "reviewer": "Reviewer_gPMn", "summary": "The motivation of this paper is very clear and has strong guiding significance for applying contrastive learning to the related field of Ordinal Regression. In view of the fact that strong data enhancement methods in existing contrastive learning will destroy or weaken localized and subtle ordinal content information, this paper proposes a generative data augmentation method that decouples ordinal content factors and non-ordinal content factors. In this method, the author adopts the principle of minimum change for variables related to ordinal content to maintain the invariance of ordinal content during the generation process. A series of experiments demonstrate the effectiveness of the proposed generative data augmentation approach.", "questions": "1. The symbols in Section 3.1 are very confusing. Some mathematical symbols add hat, some add tilde, and some add both at the same time, which is easy to confuse readers.\n2. As we all know, it is difficult to adjust the parameters of generative adversarial models in most practical applications. Therefore, I hope the authors can tell me how difficult it is to tune the parameters of the proposed generative model, which is important for practical tasks.\n3. I think the experiments in this article did not fully verify that zo is an ordinal content factor and zn is a non-ordinal content factor. For example, the former obtains wrinkles or gray hair through the generator alone, while the latter obtains other features unrelated to age. From my point of view, the experimental part can only prove that the generated data-augmented images can maintain the ordinal content, which may benefit from the powerful generation ability of the generator itself. So I'd like to see more visual experiments verify this.\n4. In the data augmentation results produced in Figure 4, in the first group of augmentations from 4 to 6 years old, although the age range of the characters remains unchanged, the gender has changed. I think the ordinal invariant data augmentation produced by the proposed model may introduce additional noise, but there is no analysis of this additional noise in the paper. Will this additional noise generally affect the results?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivation of the paper is clear and the generative data augmentation method that decouples ordinal content factors and non-ordinal content factors is quite novel. I believe that the generative data augmentation method proposed in this article is more advanced than traditional methods such as Gaussian blur and color dithering, which will provide a new sight for both ordinal regression and contrastive learning community.\n2. The manuscript is well organized and thus it is clear and easy to understand.\n3. The experiments in this paper are sufficient, which fully demonstrates the effectiveness of the model.", "weaknesses": "1. The symbols in Section 3.1 are very confusing. Some mathematical symbols add hat, some add tilde, and some add both at the same time, which is easy to confuse readers.\n2. As we all know, it is difficult to adjust the parameters of generative adversarial models in most practical applications. Therefore, I hope the authors can tell me how difficult it is to tune the parameters of the proposed generative model, which is important for practical tasks.\n3. I think the experiments in this article did not fully verify that zo is an ordinal content factor and zn is a non-ordinal content factor. For example, the former obtains wrinkles or gray hair through the generator alone, while the latter obtains other features unrelated to age. From my point of view, the experimental part can only prove that the generated data-augmented images can maintain the ordinal content, which may benefit from the powerful generation ability of the generator itself. So I'd like to see more visual experiments verify this.\n4. In the data augmentation results produced in Figure 4, in the first group of augmentations from 4 to 6 years old, although the age range of the characters remains unchanged, the gender has changed. I think the ordinal invariant data augmentation produced by the proposed model may introduce additional noise, but there is no analysis of this additional noise in the paper. Will this additional noise generally affect the results?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZjDBcwO8eg", "reviewer": "Reviewer_PxYT", "summary": "In this paper, the authors aim to improve contrastive learning’s utility for ordinal regression. They find that strong data augmentation could lead to distortion of certain discriminative information (content information) in the image, which is crucial for ordinal regression. To this end, they propose to use a generative model to create augmented images, which diverse in different styles but have the same content information as the original image. Experimental results show that this approach enhance the performance of existing methods.", "questions": "Q1. Do the performance of the model sensitive to the setting of $\\lambda_1$ in Eq. (4)?\n\nQ2. The authors choose GAN as the generative model. It seems that the proposed data generative process could also be implemented by other model families (e.g., VAE or diffusion models).\n\nQ3. Could you explain that to what extent the content information can be maintained in $\\hat{\\tilde{z}}_O$, either in theory or experiments?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The motivation of this work is clear and reasonable, which provides a valid approach to tackle the problem of potential negative effects on performance caused by excessive augmentation. The experiments are sufficient to support the effectiveness of this method.", "weaknesses": "- The improvement in effectiveness comes at the cost of increasing computational overhead. The time and space required to train the generated model is no less than (or even greater than) that of training the ordinal regression model, yet the performance gain is not so significant.\n- It is unclear whether the proposed framework could guarantee that to what extent the content information can be maintained in the invariant ordinal content factors $\\hat{\\tilde{z}}_O$, which is crucial for the quality of generated images and further affect the performance of the ordinal regression model.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "yRL9Ifk9ae", "reviewer": "Reviewer_gbdG", "summary": "In this paper, the authors concentrate on the ordinal regression task. They address the challenge that strong augmentations can often overshadow or dilute the ordinal content information. To mitigate this issue, they propose an augmentation method based on the principle of minimal change to replace the predefined strong augmentations.", "questions": "See weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1.The author has provided a clear statement of the paper's motivation.\n\n2.The paper is well-organized.", "weaknesses": "1.The experimental results appear to be based on a single run, and the author should consider conducting multiple experiments to reduce the influence of randomness. Notably, the performance on the MWR in Table 1 seems to exhibit minimal variation. The author should provide clarification regarding whether this consistency is a result of chance or if there are specific underlying reasons. Additionally, the author should explore novel ways to demonstrate the effectiveness of the proposed method.\n\n2.Further clarification is needed for the meaning of \"M\" in Formula 2. The specific process of obtaining \"zo\" through Formula 2 also requires detailed explanation, as this is a critical aspect that is currently lacking in the current version.\n\n3.Formula 4 in the paper lacks sensitivity analysis for \"lambda1.\"\n\n4.Ablation experiments should be conducted to effectively demonstrate the impact and effectiveness of the proposed method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9fZEvmdLP9", "reviewer": "Reviewer_yfaW", "summary": "The article proposes to apply contrastive learning to the ordinal regression problem and suggests that strong augmentation could eliminate task-related features when generating augmented images. To address this problem, the authors propose a plug-and-play method based on the principle of \"minimal change\" to generate the augmented images via GAN. This method could retain the desired ordinal information and consistently improve the performance of existing state-of-the-art methods in ordinal regression tasks.", "questions": "1.What is the difference between the proposed generative method and [1]?\n2.How does minimal change ensure that content information related to ordinal is preserved? \n3.Is the mask operation category-wise? if not, how does the model select whether or not to treat hair as ordinal information based on different age groups?\n4.Can you provide more generated images? The two sets of images in Figure 5 are not from the same age group. In real life, most infants do not have hair and most children have. I think that if hair is not ordinal information, it is more beneficial to improve the performance of the classifier if part of the generated images have hair while others do not have hair. What's your opinion?\n[1]Shaoan Xie, Lingjing Kong, Mingming Gong, and Kun Zhang. Multi-domain image generation and translation with identifiability guarantees. In The Eleventh International Conference on Learning Representations, 2022.", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1.The article points out the possible reliance on detail features in ordinal regression.\n2.The authors propose to generate augmented images via GAN with the guidance of “minimal change”. The augmented images are applied in contrastive learning to boost performance for ordinal regression. \n3.Experiments on multiple datasets demonstrate the improvements in performance.", "weaknesses": "1.The quality of the writing needs to be improved, and some important concepts are not explained. \n(1)“Deep Sigmoid Flow” is not explained in detail. By the way, in the reference paper, there is only “deep sigmoidal flows”. Is \"Deep Sigmoid Flow\" a typo, or is it a completely different concept than “deep sigmoidal flows”?\n(2)The detailed implementation of mask is not explained. I think it's important to clarify whether it's a category-wise operation or not.\n2.The experiments are not sufficient.\n(1)Image augmentation is not necessary in supervised contrastive learning. The article lacks comparisons with contrastive learning without image augmentation and with contrastive learning using conventional image augmentation. The benefits of additional image augmentation are unclear.\n(2)The article does not conduct experiments based on conventional GANs and does not demonstrate the superiority of the proposed generation methods.\n3.The proposed method is only appropriate for data augmentation in the supervised scenario, while most of the contrastive learning methods that have a strong dependence on augmentation are unsupervised.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (poster)