mlsanityData to 14 Oct 2026

Data Augmentation Through Random Style Replacement

arXiv 2504.10563 ↗First posted 14 Apr 2025cs.CVcited by 9

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Abstract (arXiv)

In this paper, we introduce a novel data augmentation technique that combines the advantages of style augmentation and random erasing by selectively replacing image subregions with style-transferred patches. Our approach first applies a random style transfer to training images, then randomly substitutes selected areas of these images with patches derived from the style-transferred versions. This method is able to seamlessly accommodate a wide range of existing style transfer algorithms and can be readily integrated into diverse data augmentation pipelines. By incorporating our strategy, the training process becomes more robust and less prone to overfitting. Comparative experiments demonstrate that, relative to previous style augmentation methods, our technique achieves superior performance and faster convergence.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 40 of its 61 references that are in the map. First posted on arXiv 14 Apr 2025.

Builds on

40 of 61 references.

References · 40

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  3. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  4. 2022Generative Adversarial Networkscited by 5,627
  5. 2017Random Erasing Data Augmentationcited by 1,239
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  8. 2018Style Augmentation: Data Augmentation via Style Randomizationcited by 79
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28 more
  1. 2024Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformerscited by 35
  2. 2024Regional Style and Color Transfercited by 30
  3. 2024Heterogeneous Contrastive Learning for Foundation Models and Beyondcited by 27
  4. 2021Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifierscited by 24
  5. 2024TWIN-GPT: Digital Twins for Clinical Trials via Large Language Modelcited by 24
  6. 2024Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approachcited by 24
  7. 2023Image Data Augmentation Approaches: A Comprehensive Survey and Future directionscited by 23
  8. 2024RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Textscited by 23
  9. 2019Confidence Trigger Detection: Accelerating Real-time Tracking-by-detection Systemscited by 21
  10. 2024A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentationcited by 21
  11. 2024Mitigating the Risk of Health Inequity Exacerbated by Large Language Modelscited by 21
  12. 2024Enhance Image-to-Image Generation with LLaVA-generated Promptscited by 20
  13. 2024Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environmentscited by 17
  14. 2024Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methodscited by 16
  15. 2024AR Overlay: Training Image Pose Estimation on Curved Surface in a Synthetic Waycited by 15
  16. 2024HADES: Hardware Accelerated Decoding for Efficient Speculation in Large Language Modelscited by 15
  17. 2023T-GAE: Transferable Graph Autoencoder for Network Alignmentcited by 14
  18. 2024Few-shot Name Entity Recognition on StackOverflowcited by 14
  19. 2024Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layoutcited by 14
  20. 2024Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?cited by 13
  21. 2025Adaptive Fault Tolerance Mechanisms of Large Language Models in Cloud Computing Environmentscited by 12
  22. 2023Influence Pathway Discovery on Social Mediacited by 11
  23. 2025Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learningcited by 11
  24. 2024Style Transfer: From Stitching to Neural Networkscited by 8
  25. 2024A Stochastic GDA Method With Backtracking For Solving Nonconvex Concave Minimax Problemscited by 6
  26. 2024Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchaincited by 5
  27. 2024Focused ReAct: Improving ReAct through Reiterate and Early Stopcited by 3
  28. 2024GPTDrawer: Enhancing Visual Synthesis through ChatGPTcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 8; 2026: 1.

9 papers.

  1. 2025Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healingcited by 10
  2. 2025Cross-Cloud Data Privacy Protection: Optimizing Collaborative Mechanisms of AI Systems by Integrating Federated Learning and LLMscited by 9
  3. 2025LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversioncited by 7
  4. 2025Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networkscited by 6
  5. 2025Curriculum Guided Reinforcement Learning for Efficient Multi Hop Retrieval Augmented Generationcited by 5
  6. 2025Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systemscited by 3
  7. 2025DeepRAG: Integrating Hierarchical Reasoning and Process Supervision for Biomedical Multi-Hop QAcited by 2
  8. 2025QuickMerge++: Fast Token Merging with Autoregressive Priorcited by 1
  9. 2026Evaluation of Randomization through Style Transfer for Enhanced Domain Generalizationcited by 0