SMOTE: Are We Learning to Classify or to Detect Synthetic Data?
Nada Boudegzdame
1 a
, Karima Sedki
1 b
, Rosy Tspora
2,3,4 c
and Jean-Baptiste Lamy
1 d
e Sorbonne Paris Nord, Sorbonne Universit
e, France
INSERM, Universit
e de Paris Cit
e, Sorbonne Universit
e, Cordeliers Research Center, France
HeKA, INRIA, France
Department of Medical Informatics, H
opital Europ
een Georges-Pompidou, AP-HP, France
Imbalanced Data, Oversampling, SMOTE, Data Augmentation, Class Imbalance, Machine Learning, Neural
Networks, Synthetic Data.
Oversampling algorithms are used as preprocess in machine learning, in the case of highly imbalanced data in
an attempt to balance the number of samples per class, and therefore improve the quality of models learned.
While oversampling can be effective in improving the performance of classification models on minority
classes, it can also introduce several problems. From our work, it came to light that the models learn to
detect the noise added by the oversampling algorithms instead of the underlying patterns. In this article, we
will define oversampling, and present the most common techniques, before proposing a method for evaluating
oversampling algorithms.
Oversampling is a technique used to solve the prob-
lem of class imbalance in machine learning. Class
imbalance occurs when the number of samples in one
class is much lower than the number of samples in the
other class(es). This is a problem because the classi-
fier will have a hard time learning from the minority
class. Oversampling techniques generate additional
samples belonging to the minority class so that the
classifier has a better chance of learning from them
(He and Garcia, 2009; Batista et al., 2004).
Oversampling creates new instances of the mi-
nority classes by either 1) replicating existing in-
stances or, 2) synthesizing samples, to increase its
representation in the dataset. Some popular tech-
niques include Random Oversampling (Chawla et al.,
2002), SMOTE (Chawla et al., 2002), ADASYN (He
et al., 2008), Borderline SMOTE (Han et al., 2005),
SMOTEN(Chawla et al., 2002), Safe-Level SMOTE
(Bunkhumpornpat et al., 2009), and Minority Over-
sampling Technique (MOTE) (Huang et al., 2006).
While oversampling can enhance the performance
of classification models on minority classes but brings
significant problems, especially in highly imbalanced
data. In this article, we will define the potential prob-
lems and challenges when using oversampling. A
core concern arises from the shift in dataset compo-
sition due to oversampling, where the original minor-
ity class data becomes a small portion, overshadowed
by synthetic data. This transformation fundamentally
alters the learning problem for machine learning mod-
els. Consequently, models often prioritize predicting
synthetic data, learning noise instead of underlying
minority class patterns.This can result in poor model
generalisation and performance on real-world data
(Tarawneh et al., 2022; Drummond and Holte, 2003;
Chen et al., 2004; Rodr
ıguez-Torres et al., 2022).
Consequently, we propose a method for evaluat-
ing oversampling techniques on a given dataset, with
a focus on the specific application of drug-induced
hemorrhage. The method consists in trying to learn
a model that can predict the synthetic status of the
sample; the better this model is, the worst the over-
sampling technique performs. Finally, we will put to
test the most common oversampling techniques and
evaluate their effectiveness in a practical example.
Boudegzdame, N., Sedki, K., Tspora, R. and Lamy, J.
SMOTE: Are We Learning to Classify or to Detect Synthetic Data?.
DOI: 10.5220/0012325300003636
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART 2024) - Volume 3, pages 283-290
ISBN: 978-989-758-680-4; ISSN: 2184-433X
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
Since the introduction of SMOTE over 20 years ago in
2002, numerous techniques have evolved to enhance
its effectiveness. Borderline SMOTE was among
the first improvements, mitigating SMOTE’s overfit-
ting risk by generating synthetic samples exclusively
for minority class instances near the decision bound-
ary. ADASYN followed, addressing harder-to-learn
minority samples by adapting the density distribu-
tion of the feature space. Safe-Level SMOTE selec-
tively generates synthetic samples for minority class
instances with proximate majority class neighbors to
reduce misclassification risk. The latest advancement,
SMOTEN, handles datasets with both nominal and
continuous features through a distinct approach for
synthetic sample generation in nominal features.
These oversampling techniques exhibit varying
strengths and weaknesses, with performance influ-
enced by the dataset and classification task. To further
tackle remaining challenges, several approaches have
emerged, including combining oversampling with
under-sampling, utilizing advanced synthetic sam-
pling techniques, or adjusting classification thresh-
olds. Each approach carries its unique advantages
and limitations, necessitating careful selection based
on the specific dataset and classification problem at
2.1 Known Problems of Oversampling
SMOTE, the most common (He and Garcia, 2009)
and effective (Batista et al., 2004) oversampling tech-
nique, generates synthetic minority class instances
by interpolating between existing samples and their
k nearest neighbors in the feature space. This pro-
cess enriches minority class representation without
duplication, making it a valuable tool for addressing
class imbalance in real-world datasets where one class
is severely underrepresented. However, while over-
sampling enhances model performance on minority
classes, it presents challenges that can be categorized
into six main areas:
1. First, one of the most common problems asso-
ciated with oversampling is the potential bias towards
the minority class (Tarawneh et al., 2022; Drummond
and Holte, 2003). When oversampling is applied, the
minority class is artificially inflated by creating new
synthetic samples, leading the model to become over-
reliant on this class and ignore the majority class.
This can result in high accuracy on training data but
poor performance on real-world data, given the infre-
quency of the minority class.
2. Oversampling can also lead to inconsistencies
in data types, as synthetic data points may generate
values that are outside the typical range of the vari-
able or in a different format. For example, if the orig-
inal data only contains whole numbers for age, over-
sampling may generate decimal numbers that are not
present in real-world data.
3. Synthetic samples created through oversam-
pling are assumed to belong to the minority class,
but this may not be true. It may also produce miss-
labeled samples belonging to the majority class, and
also ”noise” samples that are absurd and do not cor-
respond to any class or reality, such as a patient aged
3 and weighing 100kg.
4. The distribution of the data may also be altered
by synthetic samples. For example, if the minority
class includes 50% of children but the synthesized
data includes only 20% then the distribution is not the
5. Oversampling can reduce the diversity of the
dataset by creating synthetic samples that are very
similar to existing samples. This can result in overfit-
ting and negatively impact the model’s ability to gen-
eralize to new data. The oversampled dataset may not
accurately reflect the true diversity of the problem.
It’s important to carefully consider the impact of
oversampling on the distribution and diversity of the
dataset to ensure that the resulting model accurately
reflects the true nature of the problem.
6. Oversampling can increase the computational
cost of training a model, as it requires generating ad-
ditional data points for the minority class (Chen et al.,
2004; Rodr
ıguez-Torres et al., 2022). When working
with large datasets, where generating synthetic data
can be time-consuming and resource-intensive.
Additionally, the more imbalanced the dataset is
the less the oversampled dataset accurately reflects
the true nature of the problem (He and Garcia, 2009).
As explained above, the oversampling algorithm will
adjust the class distribution of a data set. So the more
imbalanced the dataset the more data will be a need to
adjust the class distribution as a result more synthetic
data the oversampled dataset contains. This can be
particularly challenging when working with anomaly
detection datasets since they tend to have highly im-
balanced class distributions, as the occurrence of
rare events or conditions is infrequent compared to
the overall population. Medical and fraud detection
datasets are common examples of such highly imbal-
anced datasets where detecting anomalies is critical,
but these anomalies are rare in occurrence (Chandola
et al., 2009).
Medical datasets, in particular, pose significant
challenges for oversampling techniques. These
datasets are often the most imbalanced because cer-
ICAART 2024 - 16th International Conference on Agents and Artificial Intelligence
tain diseases or conditions may be rare in compari-
son to the overall population. For instance, a specific
disease might affect only a small percentage of peo-
ple, while the majority are healthy. Consequently, the
dataset will have a highly imbalanced class distribu-
tion, with the minority class being the medical condi-
tion of interest (Longadge and Dongre, 2013).
Moreover, the expense and complexity of medical
data collection can contribute to class imbalance. Col-
lecting medical data often involves costly and time-
intensive procedures, like medical tests or imaging,
which can be difficult to perform on a large and di-
verse population. Consequently, the data collected
may be biased towards certain groups or demograph-
ics, leading to imbalanced class distributions.
In the next section, we will illustrate some prob-
lems encountered with oversampling over a medical
3.1 Description of the Initial Machine
Learning Task
Our initial goal was to predict hemorrhage risk us-
ing patient medical prescriptions from the MIMIC
database, a comprehensive resource of de-identified
electronic health records for over 40,000 ICU patients
in the United States. The database includes clinical
data like demographics, diagnoses, laboratory results,
medication details, and vital signs (Johnson et al.,
Specifically, we sought to identify patients at high
risk of hemorrhage due to specific medications, doses,
and medical histories. High-risk individuals typically
have a prior history of hemorrhage, which is a criti-
cal concern, as certain medications, dosages, and in-
dividual medical backgrounds can increase the like-
lihood of life-threatening hemorrhagic events. Com-
mon medications known to heighten hemorrhage risk
include anticoagulants like warfarin, dabigatran, and
apixaban, as well as antiplatelet agents like aspirin
and clopidogrel. Other medications, such as non-
steroidal anti-inflammatory drugs (NSAIDs) and se-
lective serotonin reuptake inhibitors (SSRIs), may
also increase the risk of hemorrhage, especially when
taken in high doses or in combination with other med-
ications (Hamrick and Nykamp, 2015).
We define the machine learning classification
problem as follows:
Predicting hemorrhage risk
Input: Medical patient prescription history, hospi-
tal patient admission history. Output: Patient has
a hemorrhage risk or not ?
To label the data, we first needed to define how
to extract information on medication-induced hemor-
rhage. We achieved this by examining the patient hos-
pital admission record, which contains the reason for
admission coded using the International Classification
of Diseases (ICD) system. This system is a standard-
ized medical classification system used for coding and
classifying medical procedures, symptoms, and diag-
noses (World Health Organization, 2016). By analyz-
ing the International Classification of Diseases sys-
tem, we were able to define a list of ICD codes that
represent medication-induced hemorrhage.
The input data included hospital admission
records and current prescription details, with medi-
cations coded using the US-specific National Drug
Code (NDC). However, NDC is specific to the US and
is too specific, since distinct codes exist for the vari-
ous dosages, forms, and presentations of a drug (U.S.
Food and Drug Administration, nd).Thus, we used
the Anatomical Therapeutic Chemical (ATC) classi-
fication system, which organizes medications based
on their therapeutic properties and anatomical site of
action (WHO Collaborating Centre for Drug Statis-
tics Methodology, 2013). To address this difference,
we mapped the NDC code to its corresponding ATC
code. Some medications have multiple ATC codes; in
this case, we considered all of them.
Finally, we coded patient’s medication using one
hot encoding. It’s a process used in machine learn-
ing to convert categorical data into a numerical rep-
resentation that can be used by machine learning al-
gorithms. It involves creating a binary vector that has
one value for each possible drug, the value being 1
if the drug is present and 0 otherwise. For exam-
ple, if there are three medications - M
, M
, and M
- each medication or drug order would be represented
by a binary vector of length three. The medication M
would be represented by the vector [1,0,0], the med-
ication M
would be represented by [0,1,0], and the
medication M
would be represented by [0,0,1]. A
drug order associating medication M
and M
be represented by [1,1,0]. This allows algorithms to
work with categorical data, which can be useful in
many applications such as text classification.
The resulting dataset was highly imbalanced with
a minority class representing only 3.47 % of patients
who have a hemorrhage risk. The imbalanced na-
ture of the dataset can pose a significant challenge
for the model in accurately predicting the minority
class. This is because the model may become biased
SMOTE: Are We Learning to Classify or to Detect Synthetic Data?
toward the majority class, which resulted in poor per-
formance when predicting the minority class. To ad-
dress this issue, we employed oversampling as a com-
mon technique to balance the dataset.
While we have presented results based on a single
dataset for clarity, it is essential to note that our ap-
proach has been tested on multiple datasets (Boudegz-
dame et al., 2024).
3.2 New Problem Encountered with
After oversampling, we observed significant improve-
ment in the model’s performance on both the training
and validation which was oversampled but performed
poorly on the original data in terms of performance
metrics. Moreover, predicting the risk of hemorrhage
is a challenging task as it occurs infrequently, and it
is difficult to predict if a prescription will result in
a hemorrhage. However, we obtained an f1 score
of 90% in predicting hemorrhage on training which
seemed too optimistic. To investigate this issue, we
conducted an analysis of the model’s predictions to
determine if it was still addressing the initial problem.
We formulate the hypothesis that the model was
learning to predict whether a sample was synthetic,
instead of predicting whether it belongs to the minor-
ity class which, indeed, is almost the same, since a
large majority of the samples belonging to the minor-
ity class are synthetic.
3.3 A Method for Measuring the
Detectability of Synthetic Data
To test this hypothesis, we defined a new machine
learning problem to detect synthetic data. We
generated a number of synthetic samples equal to
the number of samples in the minority class using
oversampling, we removed samples from the majority
class, and we labeled the samples as either synthetic
(1) or original (0). We applied this approach to
different oversampling methods. It aims at deter-
mining the ease with which synthetic data generated
by these methods could be detected, with lower-
quality data being more easily detected. Our refined
dataset was used to address the following problem:
Detecting synthetic data
Input: Minority class VS synthetic data produced
by oversampling. Output: Is the instance synthetic
or original?
In our analysis, we considered a range of evalua-
tion metrics to assess the model’s performance:
1. Precision, Recall, and F1 Score: Precision mea-
sures correct positive predictions out of all pre-
dicted positives, while recall measures correct
positives out of all actual positives. F1 score, the
harmonic mean of precision and recall, assesses
model performance, especially with highly imbal-
anced data (He and Garcia, 2009; Powers, 2011).
2. Area Under the Precision-Recall Curve
(AUPRC): A single score capturing the trade-off
between precision and recall, especially valuable
for imbalanced data as it focuses on the positive
class and can provide a more informative eval-
uation than accuracy or ROC AUC (Davis and
Goadrich, 2006).
3. Receiver Operating Characteristic (ROC) and
Area Under the Curve (AUC): ROC depicts true
positive rates versus false positive rates at var-
ious decision thresholds, while AUC condenses
this curve into a single performance score. These
metrics are valuable for comparing models with
varying thresholds (He and Garcia, 2009; Powers,
2011; Fawcett, 2006).
4. Confusion Matrix: Providing detailed insights
into true positives, true negatives, false positives,
and false negatives. This helps identify correct
and incorrect classifications for each class.
5. Cohen’s kappa: measures inter-rater agreement
between the original and oversampled datasets. It
can be useful for evaluating how well the syn-
thetic data captures the true nature of the problem
(McHugh, 2012).
When learning to predict whether samples are
synthetic, we obtain performance metric values that
indicate the success of the learning process. These
metric values serve as a measure that quantifies the
problem we discovered.
In this second learning task, it is crucial to use
the same machine learning technique as in the initial
learning process. This consistency ensures a fair test
to determine whether this technique can effectively
discern the synthetic nature of the samples, as using
a different technique may behave differently. There-
fore, we have opted for a neural network.
For the current implementation, we used a neu-
ral network with two hidden layers containing 30
and 20 neurons respectively. To prevent the issue
of ”dead” neurons in deep networks, we opted for
the LeakyReLU activation function, which has been
shown to perform well in similar applications (He
et al., 2016). The output layer was designed with a
sigmoid activation function, commonly used in binary
classification problems.
To optimize training, we employed ReduceLROn-
Plateau learning rate scheduling. This technique al-
ICAART 2024 - 16th International Conference on Agents and Artificial Intelligence
lowed us to dynamically adjust the learning rate of the
optimizer during training, based on a monitored met-
ric such as validation loss. By doing so, we were able
to help the model escape plateaus and continue to im-
prove, even as it approached convergence. Our model
was trained over 100 epochs, which was sufficient to
ensure full learning and convergence of the model.
3.4 Results and Analysis
The results in the table 1 demonstrate that the neu-
ral network performed exceptionally well in predict-
ing synthetic samples in terms of the evaluation met-
rics across all four oversampling techniques. Notably,
both precision and recall are consistently high and
similar, indicating an absence of bias toward the ma-
jority class. This suggests that the model effectively
identifies both synthetic and original data instances,
which is particularly noteworthy given that the model
was trained on data where the synthetic class repre-
sents the majority of the samples (about 95%).
The highest score was achieved for SMOTEN in
terms of f1 score, recall, precision, accuracy, co-
hen kappa, and AUC among all oversampling tech-
niques. The Borderline SMOTE algorithm also leads
to high scores in all evaluation metrics except for
AUC. Therefore, we can easily predict whether a sam-
ple is synthetic or not. This prediction is much easier
than predicting hemorrhage risk. Thus it confirms our
hypothesis: the initial model was in fact predicting the
synthetic nature of data instead of hemorrhage risk.
As explained above, the ROC curve and Precision-
Recall curve provide important information about the
performance of a binary classification model. There-
fore, we plotted both curves to obtain a more compre-
hensive evaluation of the model’s performance. Fig-
ure 1a summarise the ROC curve for the four over-
sampling algorithms. It indicates that the model has
high accuracy in distinguishing between positive and
negative samples. In fact, an AUC of 0.5 suggests a
random classification, while an AUC of 1 suggests a
perfect classification. The AUC values for SMOTE,
borderlineSMOTE and ADASYN are 0.97, indicat-
ing that the model’s performance is very close to per-
fect, with only a small number of false positives and
false negatives. Furthermore, we observed that the
oversampled data generated by SMOTEN on our data
were the easiest to detect, as confirmed by Figure 1b,
which summarises the Recall-Precision curve.
Therefore, the table 1 and the figures 1a and 1b
suggest that oversampling techniques can be easily
detected to a great extent. However, the choice of the
oversampling algorithm should depend on the specific
characteristics of the dataset and the evaluation met-
(a) ROC Curves for Oversampling Techniques.
(b) Precision-Recall Curves for Oversampling Techniques.
Figure 1: Performance Evaluation of Oversampling Tech-
niques: ROC and Precision-Recall Curves.
rics of interest.
While it’s known that oversampling algorithm
does not behave the same on different dataset, The
testing results on medical data including medical pre-
scription, which is a highly imbalanced dataset and
strongly indicates that oversampling will not be a con-
siderable technique for balancing our data. Further
analysis and experimentation may be necessary to de-
termine the most effective approach for balancing the
medical prescription dataset in question.
3.5 Understanding Why Synthetic Data
Are Easily Detected
Oversampled medication prescriptions may not accu-
rately represent real-world data, as they are easily de-
tectable by machine learning algorithms. To gain a
better understanding of this issue, we have formulated
the following hypotheses:
SMOTE: Are We Learning to Classify or to Detect Synthetic Data?
Table 1: Performance comparison of oversampling algorithms on synthetic data classification.
F1 Score Recall Precision Accuracy Cohen Kappa AUC
SMOTE 0.92 0.94 0.90 0.92 0.84 0.97
Borderline SMOTE 0.93 0.96 0.91 0.93 0.86 0.97
SMOTEN 0.97 0.99 0.96 0.97 0.95 0.99
ADASYN 0.92 0.93 0.91 0.92 0.84 0.97
Hypothesis 1: Over or under representation of
drugs in synthetic data. (Problem #4 in section
3) Medical prescriptions typically contain a limited
number of medications. However, synthetic data gen-
erated may contain a smaller or larger number of med-
ications, resulting in an under and over representation
of drugs respectfully, which could lead to discrepan-
cies between the synthetic and real-world data.
The following table 2 shows that all four over-
sampling methods (SMOTE, Borderline SMOTE,
SMOTEN, and ADASYN) have resulted in a decrease
in the mean number of ATC codes for medication in
the oversampled data compared to the original data.
This indicates an under-representation of medication
in the oversampled samples.
Table 2: Drug distribution in original and synthetic data.
Dataset Mean number of
ATC codes
Original 34.78
SMOTE 20.11
Borderline SMOTE 21.13
SMOTEN 20.76
ADASYN 19.49
Hypothesis 2: Changes in the nature of data.
(Problem #2) SMOTE can introduce small pertur-
bations to feature values in order to create synthetic
samples, which may result in non-integer or floating-
point values for discrete features (Blagus and Lusa,
2013). For example, drugs are represented as dis-
crete values of 0 or 1, indicating the presence or ab-
sence of the drug in a prescription. However, syn-
thetic data generated for the purpose of analysis may
contain drugs with continuous values, which may lead
to inaccuracies in the results.
After further investigation, we found that the ap-
plication of SMOTE, Borderline SMOTE, SMOTEN,
and ADASYN did not result in any significant
changes to the nature of the oversampled data. All
four oversampling methods applied to our data did not
alter the nature of the data.
Hypothesis 3: Inconsistencies in ATC codes.
(Problem #3) Some drugs such as aspirin have several
ATC codes, and we associated them with all of their
corresponding codes in the original data. However, in
the synthetic samples, such a drug may be associated
with only one of its codes. For instance, an aspirin
prescription might be coded as a platelet aggregation
inhibitor but not as an analgesic in the synthetic sam-
Hypothesis 4: Inconsistencies in drug associa-
tions. (Problem #3) Synthetic prescriptions generated
may include inconsistent drug associations. For in-
stance, drugs such as ramipril and enalapril, which are
both angiotensin-converting-enzyme inhibitors and
have the same effects, thus they are never associated
together. However, such inconsistencies may occur in
the synthetic samples.
In this paper, we address a common problem associ-
ated with oversampling: the risk of the machine learn-
ing model learning to detect the synthetic nature of
oversampled data rather than the original underlying
patterns. We propose a method to identify and quan-
tify this problem, focusing on the extent to which syn-
thetic data can be detected.
In the literature many studies have explored the
problems associated with oversampling and SMOTE,
however, to the best of our knowledge, none of them
neither mentioned the learning of the synthetic nature
of data nor proposed a method for quantifying it.
Tarawneh and al. (Tarawneh et al., 2022) provide
a comprehensive review of class imbalance mitigation
in machine learning, focusing on oversampling. They
highlight issues like overfitting, higher computational
costs, and reduced generalization performance. The
article also emphasizes the risks of model bias and
decreased generalization when oversampled data is
tested on original database, along with the significant
computational overhead of creating and storing syn-
thetic samples. The authors suggest alternative ap-
proaches like cost-sensitive learning and anomaly de-
tection as more effective solutions to tackle class im-
The work of R. Buda and al. (Buda et al., 2018)
investigates the impact of class imbalance on CNN
ICAART 2024 - 16th International Conference on Agents and Artificial Intelligence
performance for image classification tasks and evalu-
ates various strategies, including oversampling. They
caution that oversampling alone may not suffice for
addressing class imbalance in CNNs due to the risk of
overfitting, where models memorize training data and
perform poorly on new data. Furthermore, oversam-
pling can generate unrealistic and redundant samples,
inefficiently utilizing computational resources.
Several studies propose modifications to the
oversampling technique to mitigate these issues.
ıguez-Torres and al. (Rodr
ıguez-Torres et al.,
2022) introduce Large-scale Random Oversampling
(LRO) to address class imbalance in large datasets.
Comparisons with other oversampling methods, such
as SMOTE and Borderline-SMOTE, show that LRO
achieves higher accuracy and F1-score while be-
ing computationally efficient. The study highlights
SMOTE’s limitations, including sample diversity is-
sues and sensitivity to noise.
Overall, the literature highlights the potential lim-
itations and challenges of oversampling and SMOTE
in addressing imbalanced data in machine learning,
and suggests alternative approaches and modifica-
tions to address these issues. The presented articles
cover various aspects of oversampling and SMOTE
problems, including overfitting, performance evalu-
ation, large dataset handling, multi-class imbalance,
noise handling, and synthetic oversampling.
In conclusion, oversampling is a valuable tool for im-
proving machine learning model performance on im-
balanced datasets. However, our research highlights
the potential issues introduced by oversampling al-
gorithms, particularly in the quality of synthetic mi-
nority class data, which can lead to models learning
to predict noise rather than underlying patterns. To
address these concerns, we have proposed a novel
evaluation method that assesses and quantifies both
the effectiveness of oversampling techniques and their
potential to introduce detectable noise. By evaluat-
ing a model’s ability to differentiate synthetic data
from real data, we can identify potentially problem-
atic oversampling methods and select the most suit-
able ones for specific datasets, ultimately enhancing
model accuracy and generalizability (Boudegzdame
et al., 2024). This approach also aids in determining
the suitability of oversampling for dataset balancing.
The perspectives of this study are: 1) delimiting
the exact perimeter of the problem we discovered, in
particular testing other similar existing oversampling
techniques, such as Generative Adversarial Networks
(GANs) (Goodfellow et al., 2014), 2) improving the
measure we proposed for quantifying the detectability
of synthetic data, for instance for multi-class and/or
multi-label classification, and 3) designing new meth-
ods of oversampling that are resilient to this problem.
This work was partially funded by the French Na-
tional Research Agency (ANR) through the ABiMed
Project [grant number ANR-20-CE19-0017-02].
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