PGASL: Predictive and Generative Adversarial Semi-supervised Learning for imbalanced data
Authors: Ru Huang
Yunlong Wang
Ruoxin Li
Wei Huang
Emily Zhao
Yilian Yuan
Venue: Submitted to ICLR 2023 (2023)
Keywords: semi-supervised learning
imbalanced learning
GAN model
adversarial learning

Abstract: Modern machine learning techniques often suffer from class imbalance where only a small amount of data is available for minority classes. Classifiers trained on an imbalanced dataset, although have high accuracy on majority classes, can perform poorly on minority classes. This is problematic when minority classes are also important. Generative Adversarial Networks (GANs) have been proposed for generating artificial minority examples to balance the training. We propose a class-imbalanced semi-supervised learning algorithm PGASL which can be efficiently trained on unlabeled and class-imbalanced data. In this work, we use a predictive network which is trained adversarially for the discriminator to correct predictions on the unlabeled dataset. Experiments on text datasets show that PGASL outperforms state-of-the-art class-imbalanced learning algorithms by including both predictive network and generator. 


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PGASL: PREDICTIVE AND GENERATIVE ADVERSAR- IAL SEMI-SUPERVISED LEARNING FOR IMBALANCED DATA
Anonymous authors Paper under double-blind review
ABSTRACT
Modern machine learning techniques often suffer from class imbalance where only small amount of data is available for minority classes. Classifiers trained on an imbalanced dataset, although have high accuracy on majority classes, can perform poorly minority classes. This is problematic when minority classes are also important. Generative Adversarial Networks (GANs) have been proposed for generating artificial minority examples to balance the training. We propose a class-imbalanced cemi-supervised learning algorithm PGASL which can be ef- ficiently trained unlabeled and class-imbalanced data. In this work, use a predictive network which is trained adversarially for the discriminator to cor- rect predictions on the unlabeled dataset. Experiments on text datasets show that PGASL outperforms state-of of-the-art llass-mmallance learning algorithms by in- cluding both predictive network and generator.
INTRODUCTION
In many real world applications such medical data analysis (Cameron al. 2010), data is often imbalanced as those patients suffering from a certain disease will typically have a very small portion of the population. It is often challenging since a classifier may be trained biased towards the majority classes and lead to poor performance for the minority classes. Although the classification may have overall good performance, this is not preferable especially for some tasks when the minority classes are also important. Addressing class imbalance has become more and more important in many fields. Another feature of these kinds of data is that usually only a small amount of data is labeled, for example, patients who visit hospitals who are inder-diagnosed for certain disease, can't be labeled as negative, thus remain unlabeled. Recently, many deep neural network based semi-supervised learning (SSL) algorithms have shown their ability of utilizing unlabeled data to improve performance. However, most works only focus on one of the above challenges and class-imbalanced semi-supervised learning is still under explored.
train machine learning model
imbalanced data
In this paper we consider the rare disease detection task in healthcare (Yu et al., 2019) which is typical imbalanced semi-supervised machine learning task with binary outputs. As GANs have huge success in learning almost real data distributions through adversarial training, we propose a three-player GAN model which can generate artificial minority data well extracting minority data from the unlabeled dataset, which helps handling the class imbalance issue. We let the dis- criminator output probabilities of predictions for each class and then use predictive network, which has the same structure as the discriminator but is trained adversarially, to correct predictions of samples on the unlabeled dataset. Finally we use generator to generate minority samples so that the discriminator will be trained using more balanced data and will be less likely to bias towards the majority class. Our main contributions include:
We introduce novel semi-supervised three-players GAN model for imbalanced data.
We conduct experiments on binary text classification datasets to benchmark PGASL with previous class imbalance learning methods. Results demonstrate that proposed method can efficiently utilize unlabeled data and handle imbalanced datasets that outperform prior works.

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To the best of our knowledge, this is the first GAN-based algorithm which can handle the class imbalance issue while utilizing the unlabeled dataset at the same time.
2
RELATED WORK
Semi-supervised (SSL) techniques have been proposed to improve performance by utilizing unla- beled data. Pseudo-Labels are used as if they were true labels on unlabeled datasets {Lee et al. 2013), and the models are trained a manner of entropy minimization (Grandvaler & Bengio
2004 Consistency regularizations aim at learning similar distributions for local perturbations
unlabeled dataset (Park et al., 2018 Miyato et al. 2018). Several data augmentation techniques in combination with Pseudo-1 Labels used to create local perturbations (Berthelot et al. 2019a Sohn
jt al. 2020 Berthelot et al. 2019b).
In this paper want highlight GAN-based methods which paper mainly follows. SSL- GANs use discriminator to classify samples from different classes instead of distinguishing fake samples from real samples. To perform adversarial training, one way to directly minimize the entropy of the outputs for the discriminator which can help distinguish real samples from fake sam- ples (Springenberg) 2015). On the other hand, one can use an additional class which indicates fake samples so that the discriminator can make predictions and recognize fake samples at the same iSmemSalsmans et al. 2016; Odena 2016). Other works use an additional network to play the role of distinguishing the fake samples (Mullick et al., 2019). PAN (Hu et al., (02T) replaces the generator with a classifier which mirrors the discriminator in classification on unlabeled dataset to help clas- sify those samples which can be hardly classified by the discriminator. Although PAN was originally proposed for PU learning, can be extended to semi-supervised learning naturally. We show in this paper, keeping the generator in PAN can have better performance in the class-imbalee -mballnnedd learning setting.
The SSL methods discussed above often assume balanced dataset and may have poor performance for an imbalanced dataset especially when the unlabeled dataset is also imbalanced (He & Garcia
2009 (unn et al. 2009). Researchers have proposed several techniques for llass-imbalanced learn-
ing. Re-sampling techniques have been proposed to create a balanced dataset (Chawla et al. 2002
Barandela al. 2003 Engelmann & Lessmann 2021bb. However, oversampling may cause over-
fitting while undersampling may cause information loss (Cao et al.) 2019). To avoid such issues, researchers have also proposed to balance the training by introducing regularization terms instead of creating balanced dataset (Huang et al. 2016 Jamal et al. 20200] Cui et al. 2019).
Recently, researchers have proposed several class-imbalanced semi-supervised learning (CISSL) techniques to improve performance on imbalanced datasets. Refining the pseudo-labels through an iterative algorithm can reduce bias (Kim et al., [2200] Wei et al. (02I). Introducing an auxiliary balanced classifier(ABBC of single layer while using 0/1 mask can balance training (Lee et al.
202T.
3 METHODS
3.1 PROBLEM SETTING
Suppose we have a binary labeled dataset L (z), = 1,...,NI! .N. where x; is d-dimensional feature vector and y; E (0, 1) is the corresponding binary label. Let Ni NN NN where Np is the number of positive data and N,, is the number of negative data. We also have an where uy is also a didimensionna feature vector but the corresponding label is unknown. We denote the label ratio as p N In this paper we consider the case when p > 0.1 where most the labels are expensive to obtain. We also consider the extremely class-imbalanced situation where the imbalanced ratio 7 N.+N is very small. We assume the labeled dataset and the unlabeled dataset share the same distribution (e.g. have the same imbalanced ratio). Given the labeled dataset and unlabeled dataset in training we aim at learning a classifier f d {0,1) which can perform well on imbalanced test set.
=
Nr
unlabeled dataset 2/ = {u,, i = 1,..., N
2

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3.2 GENERATIVE ADVERSARIAL NETWORK
First proposed in (Goodfellow et al. 2014), generative adversarial networks (GANs) have shown its excellent ability of generating almost true samples which follow the real data distribution. The model simultaneously trains a generative network (often called generator) which generates samples that can be hardly distinguish from the real data fool the discriminator, and a discriminator which tells if a sample is 'real' or 'fake' (generated by generator). GAN can be often formulated as a minmax game:
minmax (DD = Rew Panea (2) (log(D()) (1))) + (s)) - D(G(=)))) D
GANs can be trained to learn the distribution of minority classes and can then be used to generate samples to oversample minority classes to create balanced dataset (Engelmann & Lessmann 2021b). Although more focus generative tasks in general, researchers also demonstrate the good perfor- mance of GANs on classification tasks by letting discriminators to make predictions for different classes rather than only distinguish fake samples from real samples. Unlike resampling techniques which create balanced dataset first and then apply classification algorithms afterward, letting dis- criminators make predictions allows generator to learn more proper distributions they trained together. Also it is easier to adjust the regions of samples which we would like the generator to generate and this is important for imbalance learning. It is often not desirable for generator only generate samples from minority classes as this may cause overfitting and generating samples from positive samples also helps classification. Denote the ratio of positive and negative samples we ex- pect the generator to generate by Pc. The choice of PG is still under investigation. In this paper, we use PG as hyper-paramete and use grid search choose the best PG-
3.3 PROPOSED MODEL ARCHITECTURE
The architecture of our framework is shown in Figure 1 We aim at balancing the training through minority oversampling, as such, we propose a GAN model structure which can generate artificial minority samples as well as extracting minority samples from the unlabeled dataset so that more minority samples will be fed into the classifier. The model contains three components, a generator G(-) which takes random noise input and learns to generate artificial samples from the real data distribution, a discriminator D(.) which makes predictions and a predictive network C(.) which mirrors the discriminator but is trained adversarially. We the same network structure for both D(-) and C(-) except the dropout rate. We found in practice, letting C(--) has larger dropout rate consider binary classification problems and therefore
gives better performance. In this paper both the predictions from ((-) and C(-) have three classes with 0 as negative, as positive and 2 as "fake". In particular, we consider the rare disease scenario where the positive class is the minority.
The discriminator (.-) first learns to classify samples from the labeled dataset correctly using stan- dard entropy minimization for supervised learning. The supervised loss is
( y) = E log (D (y/xr, xr, y 2),
2.vEC
which minimizes the negative log probability of the label, given the labeled data. Then D(-) learns to recognize the artificial samples generated by the generator G(-) by classifying the generated samples as 'fake' (2). This correspond to minimize the loss
fake (2) #EG(x)
E
Finally, D(-) has two unsupervised loss. Minimizing the first unsupervised loss
eD (x) E log (2(r))
allows D(-) to recognize the unlabeled data by maximizing the probability of a sample from unla- beled dataset is not 'ake'. Minimizing the second unsupervised loss
f* (x)
E... zEl
[o)) - logPc(z))
allows D(-) to maximize the distance between the predictions from D(-) and C(-) on the unlabeled dataset. In this paper, we also use KL divergence to measure the distance as in PAN (Hu et al. 2021). Then the total loss for D(-) is:
eD Lsup + + lake > (con + fun),
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(()
Recognize generated samples "fake"
Give correct scores
Give different score from
+ +
+
+ -
+ + -
Labeled set
Unlabeled set
Generated Samples
Give consistent score with
Generate "real samples"
C()
G(+
Figure 1: Framework of proposed architecture on semi-supervised learning, The utilities of D(-), C() and G(-) are colored in red, purple and blue respectively.
where À is a hyper-parameter for balancing the supervised loss and the unsupervised loss. To per- form adversarial training, the predictive network C(-) learns to shrink the distance between the predictions from D(-) and C(-) by maximizing fu so that when reach an equilibrium, C(-) can make predictions with large margin on the unlabeled dataset through adversarial training. Unlike PAN, we keep the existence of the generator for handling the class imbalance issue. Denote the set of positive samples as P and the set of negative samples as V To train the generator, sample data points from V and pcn data points from P where PG is hyperparameter. should be greater than if positive is the minority class. Denote the set of the (PG + 1)n data points as LG. We use the last layer of (--) as features and map the features of labeled samples and the features of generated samples. This is achieved by minimizing the following feature mapping loss:
practice, Pc
fmm
E D²(x)
E D (t)IBB.
#ECc
IEG(aI)
In addition, we use the pull away term (Zhao et al. 2016)
f(x)) f(x)) )²
Ept N(N N(N-1)
ΣΣ
(1
== i44
to increase the diversity of generated samples. Note that the functionality of C(.) is similar to G(.). The difference is that G(.) generate artificial data that is not from training dataset while C(-) extract data from the unlabeled dataset. Both C(-) and G(-) contribute to creating more positive samples for (-) to balance training.
3.4 TRAINING OF PGASL
Algorithm gives the training procedure of PGASL using stochastic optimization algorithms. The loss functions defined in Section 3.3 The algorithm trains D--), C(-) and G(-) alternatively. In practice, one can choose to run more iterations for one component in one epoch to boost perfor- mance.
RESULTS
4.1 DATA SOURCE AND PRE-PROCESSING
In this section, we tested the proposed method PGASL and compare it with state-of-the-art algo- rithms. In this paper consider the disease scenario which has binary outputs with posi-

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Algorithm PGASL for semi-supervised learning
Input: labeled dataset L, unlabeled dataset U. batch size m, hyperparameters X, PG
Output: D
1: for k steps do
2:
Train D
Sample a mini-batch (i) E L, = 1, I,...,m) from labeled dataset and Compute
supervised loss (xap((( (D))))
Sample mini-batch {(x{) E U, 1,..;, m....,m) from unlabeled dataset and Compute first unsupervised loss e (D())))
Sample a mini-batch {(x²) E u,i = 1, m....,m) from unlabeled dataset and Compute second unsupervised loss t (D(x")).
Sample noise E (0, 1) and compute fake loss (fake(D(G(z)) Update (()) by minimizing & = lsu + A(ean + (un) + l fake.
3: Train C
Sample mini-batch ((x.) E Eu,i ......m) from unlabeled dataset and update C(-) by maximizing the loss f (D(z:)).
4: Train G
Sample mini-batch {( 0) E NN i 1,..., pc ). sample mini-batch {(=}' 1) p;i=1 .....,m), m) and sample noise = E (N 1) and compute feature mapping loss f and pull away term Ept-
Update G(-) by minimizing la lim + lyc.
5: end for
tive class being the minority class. We first created imbalanced versions of two text classification datasets: Amazon polarity reviews (McAuley & Leskovec 2013) and Yelp polarity reviews (Zhang et al. 2015) using various imbalance ratio We kept all negative data with N,, data points, then we randomly selected N2 = 22,Nn Nn positive data points. For the purpose of semi-supervised learning,
we randomly selected 10% of the data as labeled data while the rest 90% data as unlabeled data for all datasets. Since the main focus of this paper is supervisd learning rather than representation learning, first used pretrained sentence transformers (Reimers & Gurevych] 2019) preprocess all the text into sentence embeddings. Then the classification algorithms are applied the same pretrained sentence embeddings.
We summarize the information of each dataset in Table 1
Table 1: Number of samples of each class each dataset
labeled neg
labeled pos
unlabel neg
unlabel pos
test neg
test pos
Amazon 7 0.1
180153
18016
1619847
161985
200000
20000
Amazon 7 = 0.05
179406
8971
1620594
81030
200000
10000
Amazon 7 = 0.01
179848
1799
1620152
16202
200000
2000
Yelp 7 =0.1
27996
2800
252004
25201
19000
1900
Yelp -0.05
28004
1401
251996
12600
19000
950
Yelp 7 = 0.01
27939
280
252061
2521
19000
190
4.2
METRICS AND BASELINES
To evaluate our method, we mainly use three metrics: the area under the receiving operator curve(AUC-ROC), the area under the precision recall curve (PR-AUC), and the brier score. The AUC-ROC score, represents the area under the curve of the true positive rate against the false pos- itive rate for each decision threshold, which evaluates a model on ranking predictions be viewed as the
general
while proper metric for imbalanced data is the PR-AUC score which average of precision scores calculated for each recall threshold. We also use the brier score which measures the accuracy of probabilistic predictions. The reason pick these three metrics is because
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the evaluations of these scores don't 't require thresholds and can be directly applied to probabilities. This is important since a threshold 0.5 usually doesn't It work well for imbalanced datasets. We compared the above metrics of the proposed method with the following baselines:
CwGAN+xgboost: This method first CWGAN (Engelmann & Lessmann, 202 l to
first oversample the imbalanced datasets and then train xgboost (Chen & Guestrin 2016) the oversampled balanced training datasets in a fully supervised learning setting.
SGAN(Odena 2016); This method learns discriminators using GAN that output class la- bels and utilize unlabeled data in a semi-supervised setting. We use the same training of generator for PGASL and SGAN.
PAN(Hu al. 202T): This method trains classifier adversarially the discriminator on unlabeled dataset to improve performance. It was originally proposed for PU learning, we extend it to semi cumeesupee learning this paper.
ABC(Lee al. 202T): This method attaches classifier to the last layer of backbone algo- rithm and uses a 0/1 mask to balance training. We use the same discriminator in PGASL the backbone algorithm for ABC.
4.3 TRAINING DETAILS
We use fully-connected neural networks with leaky relu activation (Maas et al. 2013) and dropout Srivastava et al.) 2014) for D(-) and C(-)) in PGASL. For fair comparison network structure for the networks PAN, discriminator in SGAN and the backbone ABC. We grid search for each method search for the hyper-parameters of the best PR-AUC scores which we consider as the most important metric. Then to reduce the effect of randomness in training, for each method we run 10 repeated experiments with the best
from grid search and we compare the average scores. We use pytorch(Paszke et al. 2019) for implementation and use adam optimizer (Kingma & Ba 2014) for training.
4.4 RESULTS AND ANALYSIS
The ROC-AUC scores of each algorithm on test datasets are summarized Table Except for the Yelp dataset when 7 0.05, PGASL has the best scores. Note that all the ROC-AUC scores large even for the extremely imbalanced dataset. This is because the ROC-AUC score can often give an overly-optimistic performance of classifier on imbalanced datasets. We believe the PR-AUC score is more proper in the class-imbalance setting and can better measure the performance of the algorithms. The PR-AUC scores summarized in Table We also summarize the brier scores in Table Note that unlike ROC-AUCs and PR-AUCs, the brier score works like a loss function with O representing perfect accuracy. PGASL outperforms SGAN since PGASL has an additional predictive network which increase the prediction accuracy on unlabeled dataset during training. PGASL outperforms PAN since PGASL keeps the generator generate more balanced data which benefit iaassiimallaneed learning. Moreover PGASL outperforms the oversampling technique CW- GAN+XGB and the state-of-te of-th-of-thearr class-imbalanced superviee learning algorithm ABC.
Table 2: Test ROC-AUCs of PGASL model and benchmark models. The scores are averaged over 10 experiments.
Amazon
Yelp
Algorithm
7 = 0.1
0.05
0.01
= 0.1
0.05
0.01
CWGAN+XGB
0.896
0.890
0.869
0.944
0.932
0.914
SGAN
0.904
0.895
0.869
0.954
0.942
0.938
PAN
0.906
0.895
0.866
0.954
0.941
0.938
ABC
0.903
0.902
0.892
0.954
0.941
0.923
PGASL
0.916
0.909
0.909
0.956
0.941
0.941
Next we take the Yelp polarity dataset with = 0.1 as an example and show the discriminator supervised training losses, the predictive network training losses and the generator loss in Figure

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Table Test PR-AUCs of PGASL model and benchmark models. The scores are averaged over 10 experiments.
Amazon
Yelp
Algorithm
7= 0.1
Y = 0.05
7= 0.01
7 =
?
0.05
? 0.01
CWGAN+XGB
0.589
0.447
0.1530
0.744
0.619
0.302
SGAN
0,605
0,456
0.153
0.784
0.665
0.361
PAN
0.612
0.458
0.151
0.785
0.663
0.354
ABC
0.616
0,487
0.194
0.782
0.661
0.336
PGASL
0.646
0.511
0.210
0.787
0.668
0.371
Table 4: Test brier score of PGASL model and benchmark models. The scores are averaged over 10 experiments. Smaller scores indicate better performance.
Amazon
Yelp
Algorithm
y= 0.1
Y 0.05
Y 0.01
Y 0.1
Y 0.05
0.01
CWGAN+XGB
0.0543
0.0342
0.0094
0.0445
0.0299
0.0089
SGAN
0.0567
0.0356
0.0100
0.0378
0.0253
0.0079
PAN
0.0552
0.0362
0.0099
0.0376
0.0255
0.0078
ABC
0.0520
0.0324
0,0088
0.0377
0.0260
0.0079
PGASL
0.0501
0.0316
0.0084
0.0371
0.0251
0.0076
We can see that the discriminator ((-) can successfully learn the correct classification on labeled dataset and the generator G(-) learns generate diverse samples with similar features the real data. The predictive network C(-) trained adversarially, although was beat by D(-) at the beginning of training. reach an equilibrium eventually.
210
07
Figure Training losses for D(-).C(-) and G(-))
4.5 VISUALIZATION FOR GENERATOR
Finally use t-SNE (Van der Maaten & Hinton 2008 to visualize the samples generated by the generator learned above. For the purpose of better visualization, we choose the same number of positive and negative data points from the imbalanced labeled dataset. The visualization result is shown in Figure We can see that the the generator can generate both positive and negative samples which are in the positive and negative clusters of labeled data points respectively.

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100
50
comp-2
label
Real Positive
Real Negative
Fake Positive
Fake Negative
-100
-60
-40
-20
20
40
60
comp-1
Figure 3: T-sne visualizations where red points and green points represent samples from labeled dataset while blue points and purple points represent artificial samples from generator.
5 CONCLUSIONS
In this paper, we proposed novel GAN-based algorithm called PGASL. PGASL three-player model which has an additional predictive network. Unlike other GAN-based algorithms where the adversarial trainings are only performed by discriminators against a generator, the predictive net- work in PGASL is trained adversarial the discriminator to boost the performance the unlabeled dataset. We also showed in the paper that PGASL outperforms other GAN-based models as well as state-of-the- -fttt---rttttttt flass-imbllanee future work will be further improve the performance of PGASL by developing more robust training for the generator.
remi-supervised learning algorithms imbalanced datasets. Our
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10

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Under review as a conference paper at ICLR 2023
Xiang Zhang, Junbo Zhao, and Yann LeCun. Character-level Convolutional Networks for Text Classification. arXiv:1509.0162 [cs], September 2015.
Junbo Zhao, Michael Mathieu, and Yann LeCun. Energy-based generative adversarial network. arXiv preprint 16Xiv:1609 2016.


Reviews:
[{"review_id": "tzmM6329-z3", "reviewer": "Reviewer_QruZ", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The authors proposed a three-player GAN-based model called PGASL for class-inbalanced data. The model was tested against several existing models for class-imbalanced data using two text datasets and showed superior performance.\n", "summary_of_the_review": "The authors proposed a three-player GAN-based model called PGASL for class-inbalanced data. The model was tested against several existing models for class-inbalanced data using two large text datasets and showed superior performance. However, actual distribution of the fake data needs more careful consideration, and the other type of data (e.g., image) needs to be tested.\n", "strength_and_weaknesses": "Strength\n\n- The proposed strategy seems straightforward.\n\nWeaknesses\n\n- Why was the model tested only using text datasets? As the authors mentioned in the introduction, class-imbalance data exist in many modality.\n\n- In figure 3, while the distribution of fake positive data is well overlapped with the real positive, there are two clusters in fake negative and only one of them is overlapped with the real negative. But the authors didn’t discuss this point.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "VoaE8D_pmvf", "reviewer": "Reviewer_2CqW", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The manuscript proposes to tackle the imbalanced class distribution in semi-supervised learning. Motivated by the overfit on minority class, the author introduce a game model with three players, namely discrimintor D, generator G and Predictor C. In the training, being different with normal GAN pipeline, a new game between D and C is also introduced. Then, extensive experiments are conducted and demonstrate the superior results.", "summary_of_the_review": "This review is terrible for me. The submitted manuscript even cannot clarify their opinion. And some components like figures and tables need to be polished. I think the manuscript is not ready to be published.", "strength_and_weaknesses": "Strength:\n1. The three-players game for GAN seems to be new, as far as I am concerned.\n2. The manuscript has closed form, in which the motivation, proposal and demonstration are all mentioned, despite that some of them are not clarified.\n\nWeakness(Issues):\n1. The motivation is confusing. What is the relationship between the paper with disease? The introduction is filled with rare disease detection, but the conducted datasets are all related to text. I think the authors didn' t get how to write an acdemic paper.\n2. The reason for introducing the predictor C is missed. As shown in introduction, the manuscript is to tackle the unbalancedly distributed class, then, how the C tackles the issue? Why maximizing distance between C with D faciliates the training on rare class?\n3. How the G in the manuscript tackles the bias generation of normal G in GAN? Why the G in this paper wouldn' t be influenced by class bias?\n4. The draft is not qualified. The figures and typesetting need to be further improved.\n5. The necessary analysis in experiments are missed. For example, the demonstration for adopted datasets. What' s the real ratio of positive samples in the dataset? \n6. The conclusion is weak. The author focus on comparing with GAN. Despite that GAN is actually the most related work to the manuscript, the motivation is on addressing unbalanced class in semi-supervised learning. There lacks elaboration on that point.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "Hk3Yon-geuS", "reviewer": "Reviewer_wJ3B", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "- The authors tackled an interesting and practical problem, called semi-supervised learning with imbalance data.\n- The authors utilized the modified version of adversarial training to tackle this problem. More specifically, the authors proposed an adversarial training framework with 3 components: discriminator, generator, and predictor.\n- The proposed method achieved consistently better performances in comparison to the alternatives on multiple text datasets.", "summary_of_the_review": "1. Semi-supervised anomaly detection\n- There are some works that directly tackled the semi-supervised anomaly detection problem.\n- For instance, https://arxiv.org/abs/1906.02694 and https://arxiv.org/abs/2203.02034\n- The authors should also include these as the related works because the problems are exactly the same.\n\n2. Adversarially trained classifier.\n- It is unclear why the discriminator needs to maximize the distance between the predictions from D and C.\n- Would be good to clarify the motivations of this loss.\n- Also, why is the adversarially trained classifier useful for correct predictions on the unlabeled data?\n- How to extract data from the unlabeled data using C?\n\n3. Paper writing\n- The paper writing can be significantly improved. Many details are missing.\n- So, at the end, the authors use D? or C?\n- How to train the model with extracted minorities from unlabeled data?\n\n4. Other data domain\n- Based on the description, the proposed method (PGASL) can be applicable to any type of data.\n- In that point of view, it would be good to have other results with other data domains in addition to text data. (Such as tabular data like fraud detection)\n- As the authors mentioned, a rare disease dataset would be one of the possible datasets.\n\n5. Generative models for generating minority samples\n- If the number of minority samples are limited, how can we train the reasonable generative model that can generate those minority samples?\n\n6. Experiments\n- It would be interesting if the authors show the performance with different values of \\rho (labeled: unlabeled data ratios).\n- TSNE results seem somewhat confused. Some purble samples are actually out of distribution space.\n\n7. Etc.\n- Any insights that a larger dropout ratio on C is better?\n- Typo in l^*_{un} equation. No equal sign", "strength_and_weaknesses": "Strength:\n- The authors tackled an important problem which is somewhat under-explored.\n- The experimental results are promising.\n\nWeakness:\n- The paper writing can be significantly improved. Many details (especially on the methodology part) are missing.\n- The motivations and intuitions are somewhat missing. How C can help D is not well explained.\n- Some important related works are missing, especially about semi-supervised anomaly detection.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "7SxtE9INvie", "reviewer": "Reviewer_cLsr", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper considers the problem of semi-supervised binary classification of imbalanced data. It proposes a training method inspired by the GAN and evalutes using two text datasets.", "summary_of_the_review": "With limited contribution on the proposed algorithm and limited empirical evaluation, I recommend to reject the paper.\n", "strength_and_weaknesses": "Strength\n- This paper considers a practically important problem setting for real-world applications.\n- This paper trying to take advantage of GAN on semi-supervised learning, which is inspiring.\n\nWeakness\n- The proposed method is not well motivated and not well described. Usually discriminator is used to output two classes \"true\" of \"fake\", but is set to output N + 1 classes including \"fake\". Are there any precedents in the literature?\n- Why the predictive network is necessary as the discriminator can make class predictions?\n- Empirical evaluation is limited as only two binary and text datasets are used.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject