Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision

Kunyuan Ma; Mohan Jiang; Siyuan Han; Yaxin Liang; Yuan Chen; Zhen Xu

REVIEW 3 major objections 5 minor 5 cited by

Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper claims that adding GAN-generated synthetic minority samples improves risk-prediction accuracy and F1 across Random Forest, XGBoost, MLP, and LSTM on imbalanced financial supervision data, with LSTM reaching 80.5% accuracy and…

desk verdict Routine GAN application with an inconsistent baseline table; the central improvement claim is not currently measurable. read the letter →

arxiv 2412.15222 v1 pith:KQYIPFAE submitted 2024-12-04 q-fin.CP cs.LG

Mohan Jiang , Yaxin Liang , Siyuan Han , Kunyuan Ma , Yuan Chen , Zhen Xu This is my paper · ORCID

classification q-fin.CPcs.LG

keywords generativeadversarialnetworksdataimbalancefinancialmarketsupervisionriskpredictionsyntheticoversamplingundersamplingminorityclass

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims that generative adversarial networks can fix the class-imbalance problem that plagues financial market supervision by creating synthetic samples of rare, high-impact events. Training a GAN on the minority class and adding its output to the training set improved accuracy and F1 for all four tested models, with LSTM rising from 78.6% to 80.5% accuracy and from 77.9% to 79.8% F1. In a direct comparison on LSTM, GAN augmentation beat undersampling (76.3/77.7), oversampling (78.7/78.5), and SMOTE (79.0/78.5). The implied benefit for regulators is a model that catches more of the rare fraud, manipulation, and systemic-risk events without sacrificing performance on normal market data.

What carries the argument

The machinery is a generative adversarial network (GAN), a pair of networks in which a generator produces synthetic data from noise while a discriminator tries to tell synthetic from real; training alternates the minimax objective $\min_G \max_D V(D,G) = \mathbb{E}_{x \sim p_{\mathrm{data}}}[\log D(x)] + \mathbb{E}_{z \sim p_z}[\log(1-D(G(z)))]$. The paper's design choice is to train the GAN on minority-class samples so the generator's distribution matches the minority distribution, $p_g(z) = p_m(x)$, and then append the generated minority samples to the training set. That generator-plus-augmentation loop is what carries the reported accuracy gains.

What would settle it

Train a binary classifier to separate GAN-generated minority samples from held-out real minority samples; high separation accuracy would show the generated data do not match the minority distribution. Alternatively, run the same LSTM pipeline on a public imbalanced financial dataset and compare GAN augmentation with SMOTE; if the GAN version fails to beat SMOTE on both accuracy and F1, the claimed advantage does not hold there.

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Extended reading notes

Core claim

The paper's central claim is that GAN-generated minority-class data outperforms conventional resampling for imbalanced financial risk prediction. The paper assumes the minority data follow distribution $p_m(x)$, and trains the generator so its output distribution $p_g(z)$ satisfies $p_g(z) = p_m(x)$. With the GAN then used to augment the training data, Random Forest accuracy/F1 went from 69.3/68.7 to 70.1/69.5, XGBoost from 71.0/70.5 to 72.1/71.9, MLP from 73.2/73.1 to 75.4/75.6, and LSTM from 78.6/77.9 to 80.5/79.8. In the LSTM comparison across imbalance treatments, GAN achieved the best accuracy and F1, surpassing SMOTE, oversampling, undersampling, and the unaugmented baseline. The paper concludes that GAN-generated data is closer to the real market environment and improves the generalization of risk-prediction models for market supervision.

Load-bearing premise

The method works only if the GAN's synthetic minority samples genuinely resemble real rare financial events; the paper never verifies that resemblance.

Editorial extensions

If this is right

Gains are larger for the deep models: MLP gains 2.2 in accuracy and 2.5 in F1, LSTM gains 1.9 in both, while Random Forest gains 0.8, suggesting complex deep models benefit most.
GAN augmentation outperformed SMOTE, oversampling, and undersampling on the LSTM comparison (80.5/79.8 versus 79.0/78.5 for SMOTE), so for scarce minority classes it may be the preferred balancing strategy.
Because only minority-class samples are needed to train the generator, the approach can augment heavily skewed regulatory datasets without discarding majority-class information.
Applied to market-supervision data, the method would let regulators train models that detect more fraud, manipulation, and systemic-risk events without lowering overall accuracy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper provides no check that GAN-generated minority samples actually resemble real rare financial events; a classifier that can separate synthetic from real minority samples would expose this, and the improvement would then reflect dataset artifacts rather than true minority patterns.
The reported absolute gains are small (best F1 improvement is 1.9 points), so on different datasets or with a different GAN variant the ordering against SMOTE could reverse; the 'significant advantage' may not generalize beyond this setup.
A natural extension the paper does not test is a Wasserstein or conditional GAN, which could give better control over the generated minority distribution and possibly larger gains.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You asked for my read on arXiv:2412.15222. The short version: the reader's REJECT verdict is right, and the stress-test note lands exactly where it should. The paper applies a textbook GAN to an underspecified financial dataset and reports modest accuracy/F1 gains over resampling baselines. That is a routine extension, not a new result.

What the paper does reasonably well: the experimental design compares GAN augmentation against SMOTE, oversampling, and undersampling on the same LSTM backbone, which is the right kind of control. The reported gains are small (roughly one to two points), which at least tempers the authors' own overblown language. The method section correctly states the standard minimax objective, even if it adds nothing new.

The soft spots are serious and, in one case, load-bearing. Table 1 reports the LSTM baseline as 78.6% accuracy and 77.9% F1; Table 2 reports the same nominal LSTM baseline as 75.5% and 74.9%. That is not a formatting slip. It changes the claimed improvement from 1.9 points to 5.0 points, and it flips the relative ranking of Under-sampling versus Over-sampling depending on which baseline you trust. The paper gives no code, no data description beyond 'thousands of samples' and 'over 20 features,' no class ratio, no train/test split, no standard deviations, and no statistical tests. So there is no way to decide which baseline is real or whether any gap is noise. I also agree with the stress-test's second point: the mechanism relies on the generator approximating the minority distribution, yet there is no fidelity check, no synthetic-versus-real comparison, no error analysis. Even if the baseline were fixed, that gap would remain.

The citation pattern is weak but not disqualifying on its own. Reference [2] is a self-citation to the first author's related work; that is common and not a red flag by itself. The related work section pads with tangential GAN applications, which suggests a thin literature engagement, but that is a minor issue compared to the empirical problems.

Who is this paper for? Possibly a student or practitioner wanting a template for GAN-based oversampling. As a research contribution, it does not meet the bar. The central claim is unfalsifiable as presented because the baseline inconsistency undermines every measured gain.

My recommendation: do not send to peer review in its current form. Desk reject. If the authors later provide a corrected, consistent baseline, full dataset description, hyperparameters, code, and ideally some fidelity check on the synthetic samples, then it might be worth a look as a workshop-level application paper. Right now the evidence is not there.

Referee Report

3 major / 5 minor

Summary. The paper proposes using a generative adversarial network to synthesize minority-class samples for financial risk prediction, with the goal of mitigating class imbalance in datasets used by financial regulators. The authors evaluate the approach by augmenting training data for Random Forest, XGBoost, MLP, and LSTM classifiers, and report accuracy and F1 improvements over unaugmented baselines. They also compare GAN-based augmentation with undersampling, oversampling, and SMOTE on an LSTM model. The central claim is that GAN-generated data significantly improves prediction accuracy and F1 for imbalanced financial data.

Significance. If the reported improvements were reproducible and statistically validated, the paper would address a practically important problem in financial supervision. The use of GANs for class-imbalance augmentation is a plausible and increasingly common idea, and the comparison to traditional resampling methods is a sensible evaluation design. However, the manuscript as written provides no reproducible experimental protocol, no error analysis, and, critically, an internally inconsistent baseline for the headline LSTM result. The evidence therefore does not currently support the claim of significant advantage, and the paper's contribution is weakened by the absence of any verification that the GAN-generated samples actually resemble the true minority-class distribution.

major comments (3)

[Section IV-B, Tables 1 and 2] The LSTM baseline is reported inconsistently: Table 1 gives LSTM Acc=78.6, F1=77.9, while Table 2 gives Original(LSTM) Acc=75.5, F1=74.9. Both entries are described as the same unaugmented LSTM on the same dataset, so the GAN improvement is either 1.9 (80.5-78.6) or 5.0 (80.5-75.5) accuracy points depending on which baseline is used. The paper does not reconcile this discrepancy, and it also changes the relative ordering of GAN against under-sampling and over-sampling. This inconsistency must be resolved before the headline claim is measurable.
[Section IV-A and IV-B] The experiments are described without a dataset size, minority-class ratio, feature details, train/test split, hyperparameters, random seeds, or number of runs. No standard deviations, confidence intervals, or statistical significance tests are reported. The observed gains are small (for example, Random Forest improves from 69.3 to 70.1 accuracy in Table 1), and without repeated runs or error bars these differences could easily arise from run-to-run variation or a particular data split. The claim that GAN data yields 'significant' improvements is therefore unsupported as written.
[Section III] The method assumes p_g(z) = p_m(x), i.e., that the generated data matches the minority-class distribution, but the paper never verifies this assumption. There is no comparison between synthetic and real minority samples, no analysis of mode collapse, and no held-out evaluation on real minority events. If the generator produces unrealistic or mode-collapsed samples, the accuracy gains in Table 2 could result from the classifier learning synthetic artifacts rather than from a better representation of the true minority class. The paper needs a fidelity check, such as a distributional comparison or an evaluation on real minority data, to support the mechanism it claims.

minor comments (5)

[Section III] The GAN objective function appears garbled in the text; the equation with E_{x~p_data} and E_{z~p_z} should be typeset properly so that the reader can follow the min-max formulation.
[Section IV-B, Figure 2] Figure 2 lacks axis labels and does not indicate whether it plots the generator loss, discriminator loss, or both; please clarify.
[Section IV-A] The dataset description says 'thousands of samples' but gives no exact size or class ratio; please report the number of records and the fraction of minority-class instances.
[Section IV-B] The text refers to 'defaulting customers,' but the stated task is detecting market irregularities and systemic risks; please use consistent terminology with the dataset description.
[References] Several cited works in the Related Work section (e.g., [3]-[8] on UI design and knowledge reasoning) appear only tangentially related to financial data imbalance; please either connect them explicitly to the method or remove them.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the GAN augmentation claim is evaluated against external baselines and is not forced by construction.

full rationale

The paper's derivation chain is self-contained rather than circular. The GAN objective in Section III is the standard min-max game between generator and discriminator, with generator loss L_G = -E_{z~p_z(z)}[log D(G(z))] and discriminator loss L_D = -E_{x~p_data(x)}[log D(x)] - E_{z~p_z(z)}[log(1-D(G(z)))]. These are textbook definitions, not constructed from the target accuracy or F1 metrics, so the experimental outcome in Tables 1 and 2 is not encoded in the method. The condition p_g(z) = p_m(x) is stated as a modeling goal for the generator, not as an equation that algebraically implies the reported improvements. The evaluation is comparative: augmented Random Forest, XGBoost, MLP, and LSTM results are measured against unaugmented baselines and against under-sampling, over-sampling, and SMOTE, so the central claim would fail if GAN-generated samples did not help. Reference [2] is a self-citation by the first author, but it appears only as introductory context for why GANs can address imbalance and is not used to justify any equation, uniqueness claim, or experimental protocol; therefore it is not load-bearing. The separate concern that Tables 1 and 2 report inconsistent LSTM baselines (78.6%/77.9% versus 75.5%/74.9%) and that no fidelity check validates p_g(z) = p_m(x) is a reproducibility and validity risk, not a circularity of the derivation. Because the central claim is not equivalent to the model's assumptions or to a fitted parameter renamed as a prediction, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new entities or explicit fitted parameters. Its central claim rests on assumptions about the dataset labels, the fidelity of GAN-generated samples, and the sufficiency of single-run metrics. These assumptions are not verified in the text.

assumptions (3)

domain assumption The dataset assembled from SEC EDGAR, FINRA TRACE, and FRED contains reliable labels for high-risk financial events and is representative of regulatory supervision data.
Section IV-A describes the dataset sources but gives no labeling procedure, class ratio, or sample counts, so the validity of the labels is assumed.
domain assumption GAN-generated synthetic minority samples follow the true minority distribution, p_g(z) = p_m(x), after training.
Section III states this equality as the goal of generation but provides no distributional fidelity test, no mode-collapse analysis, and no inspection of generated samples.
ad hoc to paper A single run of each model with the reported accuracy and F1 metrics is sufficient evidence of improvement.
Section IV-B reports one set of numbers per model with no variance, no repeated seeds, and no significance testing, yet the conclusion claims significant advantages.

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Cite this review

Pith. "Pith review of Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision." pith.science (2026). https://pith.science/paper/KQYIPFAE

@misc{pith2026241215222,
  author       = {Pith},
  title        = {Pith review of: Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KQYIPFAE}},
  note         = {Machine review of arXiv:2412.15222}
}

read the original abstract

This study explores the application of generative adversarial networks in financial market supervision, especially for solving the problem of data imbalance to improve the accuracy of risk prediction. Since financial market data are often imbalanced, especially high-risk events such as market manipulation and systemic risk occur less frequently, traditional models have difficulty effectively identifying these minority events. This study proposes to generate synthetic data with similar characteristics to these minority events through GAN to balance the dataset, thereby improving the prediction performance of the model in financial supervision. Experimental results show that compared with traditional oversampling and undersampling methods, the data generated by GAN has significant advantages in dealing with imbalance problems and improving the prediction accuracy of the model. This method has broad application potential in financial regulatory agencies such as the U.S. Securities and Exchange Commission (SEC), the Financial Industry Regulatory Authority (FINRA), the Federal Deposit Insurance Corporation (FDIC), and the Federal Reserve.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

TailBooster: A Dual-Layer Generative Framework for Extreme Value Augmentation with Operational Validity Enforcement
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TailBooster augments mixed-type tabular data by generating from IQR-defined extreme subsets and cleaning with autoencoder anomaly detection, improving extreme-value regression utility.
Beyond the Norm: A Survey of Synthetic Data Generation for Rare Events
cs.LG 2025-06 accept novelty 4.0 of 10

A review of synthetic data generation for extreme events that compiles methods, datasets, and an evaluation framework focused on extremeness rather than privacy.
Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data
cs.LG 2025-02 reject novelty 3.0 of 10

The paper claims that GNN embeddings plus hierarchical mining improve frequent-pattern discovery for minority classes on imbalanced tabular data.
A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models
cs.LG 2025-02 reject novelty 2.0 of 10

A standard Markov network with class weights reportedly beats four baselines on a credit card fraud dataset, but the model is not specified enough to verify.
Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Models
cs.CL 2025-01 reject novelty 2.0 of 10

Dynamic LoRA, a layer-wise adaptive variant of LoRA, reportedly improves GLUE accuracy from 87.4% to 88.1% at only 0.1% more trainable parameters, but the write-up lacks reproducibility.

Reference graph

Works this paper leans on

20 extracted references · 14 canonical work pages · cited by 5 Pith papers

[2]

Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment

M. Jiang, J. Lin, H. Ouyang, et al., "Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment," arXiv preprint arXiv:2409.18544, 2024

work page Pith review arXiv 2024
[1]

Design of a Personal Credit Risk Prediction Model and Legal Prevention of Financial Risks,

Y. Zhuang, and H. Wei, "Design of a Personal Credit Risk Prediction Model and Legal Prevention of Financial Risks," IEEE Access, 2024

work page 2024
[3]

Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning,

S. Duan, Z. Wang, S. Wang, M. Chen, and R. Zhang, "Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning," arXiv preprint, arXiv:2411.06326, 2024

arXiv 2024
[4]

Efficient and Aesthetic UI Design with a Deep Learning-Based Interface Generation Tree Algorithm,

S. Duan, R. Zhang, M. Chen, Z. Wang, and S. Wang, "Efficient and Aesthetic UI Design with a Deep Learning-Based Interface Generation Tree Algorithm," arXiv preprint arXiv:2410.17586, 2024

arXiv 2024
[5]

Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation,

Y. Dong, S. Wang, H. Zheng, J. Chen, Z. Zhang, and C. Wang, "Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation," arXiv preprint arXiv:2411.03572, 2024

arXiv 2024
[6]

Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems,

J. Chen, R. Bao, H. Zheng, Z. Qi, J. Wei, and J. Hu, "Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems," arXiv preprint arXiv:2410.14167, 2024

arXiv 2024
[7]

Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm

G. Huang, A. Shen, Y. Hu, J. Du, J. Hu, and Y. Liang, "Optimizing YOLOv5s Object Detection through Knowledge Distillation Algorithm," arXiv preprint arXiv:2410.12259, 2024

work page Pith review arXiv 2024
[8]

Dual-Branch Dynamic Graph Convolutional Network for Robust Multi-Label Image Classification,

B. Wang, H. Zheng, Y. Liang, G. Huang, and J. Du, "Dual-Branch Dynamic Graph Convolutional Network for Robust Multi-Label Image Classification," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 5, pp. 94-99, 2024

work page 2024

Show all 20 references

[9]

Modified-generative adversarial networks for imbalance text classification,

P. Rani, and O. P. Verma, "Modified-generative adversarial networks for imbalance text classification," Multimedia Tools and Applications, pp. 1-20, 2024

2024
[10]

Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction,

Z. Zhao, T. Cui, S. Ding, et al., "Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction," Mathematics, vol. 12, no. 5, pp. 701, 2024

2024
[11]

The Impact of Large Interest Rate Differentials between China and the US on the Role of Chinese Monetary Policy--Based on Data Model Analysis,

J. Yao, "The Impact of Large Interest Rate Differentials between China and the US on the Role of Chinese Monetary Policy--Based on Data Model Analysis," Frontiers in Economics and Management, vol. 5, no. 8, pp. 243-251, 2024

2024
[12]

Fraud Detection in Credit Risk Assessment Using Supervised Learning Algorithms,

T. Xu, "Fraud Detection in Credit Risk Assessment Using Supervised Learning Algorithms," Computer Life, vol. 12, no. 2, pp. 30-36, 2024

2024
[13]

A Hybrid CNN-LSTM Model for Enhancing Bond Default Risk Prediction,

J. Yao, J. Wang, B. Wang, B. Liu, and M. Jiang, "A Hybrid CNN-LSTM Model for Enhancing Bond Default Risk Prediction," Journal of Computer Technology and Software, vol. 3, no. 6, 2024

2024
[14]

Adaptive Feature Interaction Model for Credit Risk Prediction in the Digital Finance Landscape,

Y. Wu, K. Xu, H. Xia, B. Wang, and N. Sang, "Adaptive Feature Interaction Model for Credit Risk Prediction in the Digital Finance Landscape," Journal of Computer Science and Software Applications, vol. 3, no. 1, pp. 31-38, 2023

2023
[15]

A Recommendation Model Utilizing Separation Embedding and Self- Attention for Feature Mining,

W. Liu, R. Wang, Y. Luo, J. Wei, Z. Zhao, and J. Huang, "A Recommendation Model Utilizing Separation Embedding and Self- Attention for Feature Mining," arXiv preprint arXiv:2410.15026, 2024

2024 arXiv
[16]

Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments,

P. Li, Y. Xiao, J. Yan, X. Li, and X. Wang, "Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments," arXiv preprint arXiv:2411.05346, 2024

2024 arXiv
[17]

Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining,

X. Yan, Y. Jiang, W. Liu, D. Yi, and J. Wei, "Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining," arXiv preprint, arXiv:2409.14327, 2024

2024 arXiv
[18]

Self- Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks,

J. Wei, Y. Liu, X. Huang, X. Zhang, W. Liu, and X. Yan, "Self- Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks," arXiv preprint arXiv:2410.17617, 2024

2024 arXiv
[19]

Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications,

S. Liu, G. Liu, B. Zhu, Y. Luo, L. Wu, and R. Wang, "Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications," arXiv preprint arXiv:2410.08553, 2024

2024 arXiv
[20]

Transformers in Opinion Mining: Addressing Semantic Complexity and Model Challenges in NLP,

J. Du, Y. Jiang, and Y. Liang, "Transformers in Opinion Mining: Addressing Semantic Complexity and Model Challenges in NLP," Transactions on Computational and Scientific Methods, vol. 4, no. 10, 2024

2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [2]

Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment

M. Jiang, J. Lin, H. Ouyang, et al., "Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment," arXiv preprint arXiv:2409.18544, 2024

work page Pith review arXiv 2024

[2] [1]

Design of a Personal Credit Risk Prediction Model and Legal Prevention of Financial Risks,

Y. Zhuang, and H. Wei, "Design of a Personal Credit Risk Prediction Model and Legal Prevention of Financial Risks," IEEE Access, 2024

work page 2024

[3] [3]

Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning,

S. Duan, Z. Wang, S. Wang, M. Chen, and R. Zhang, "Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning," arXiv preprint, arXiv:2411.06326, 2024

arXiv 2024

[4] [4]

Efficient and Aesthetic UI Design with a Deep Learning-Based Interface Generation Tree Algorithm,

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arXiv 2024

[5] [5]

Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation,

Y. Dong, S. Wang, H. Zheng, J. Chen, Z. Zhang, and C. Wang, "Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation," arXiv preprint arXiv:2411.03572, 2024

arXiv 2024

[6] [6]

Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems,

J. Chen, R. Bao, H. Zheng, Z. Qi, J. Wei, and J. Hu, "Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems," arXiv preprint arXiv:2410.14167, 2024

arXiv 2024

[7] [7]

Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm

G. Huang, A. Shen, Y. Hu, J. Du, J. Hu, and Y. Liang, "Optimizing YOLOv5s Object Detection through Knowledge Distillation Algorithm," arXiv preprint arXiv:2410.12259, 2024

work page Pith review arXiv 2024

[8] [8]

Dual-Branch Dynamic Graph Convolutional Network for Robust Multi-Label Image Classification,

B. Wang, H. Zheng, Y. Liang, G. Huang, and J. Du, "Dual-Branch Dynamic Graph Convolutional Network for Robust Multi-Label Image Classification," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 5, pp. 94-99, 2024

work page 2024