# Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model

arXiv: https://arxiv.org/abs/2403.19600 · HTML: https://mlsanity.com/paper/2403.19600/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 47 of its 72 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 47)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation](https://mlsanity.com/paper/2011.12799/) (2020)
- [Synthetic Data from Diffusion Models Improves ImageNet Classification](https://mlsanity.com/paper/2304.08466/) (2023)
- [Is synthetic data from generative models ready for image recognition?](https://mlsanity.com/paper/2210.07574/) (2022)

## Built on by (most cited first, 30 of 34)

- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [A Survey on Mixup Augmentations and Beyond](https://mlsanity.com/paper/2409.05202/) (2024)
- [Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation](https://mlsanity.com/paper/2406.14551/) (2024)
- [Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification](https://mlsanity.com/paper/2408.16266/) (2024)
- [Diffusion-based Synthetic Data Generation for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2503.12472/) (2025)
- [Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery](https://mlsanity.com/paper/2507.04051/) (2025)
- [Context-guided Responsible Data Augmentation with Diffusion Models](https://mlsanity.com/paper/2503.10687/) (2025)
- [GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model -- Bringing Motion Generation to the Clinical Domain](https://mlsanity.com/paper/2503.22397/) (2025)
- [Decoupled Data Augmentation for Improving Image Classification](https://mlsanity.com/paper/2411.02592/) (2024)
- [MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning](https://mlsanity.com/paper/2504.07288/) (2025)
- [SGD-Mix: Enhancing Domain-Specific Image Classification with Label-Preserving Data Augmentation](https://mlsanity.com/paper/2505.11813/) (2025)
- [ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification](https://mlsanity.com/paper/2507.11845/) (2025)
- [MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised Representations](https://mlsanity.com/paper/2406.12368/) (2024)
- [Beyond One-Hot Labels: Semantic Mixing for Model Calibration](https://mlsanity.com/paper/2504.13548/) (2025)
- [Do We Need All the Synthetic Data? Targeted Image Augmentation via Diffusion Models](https://mlsanity.com/paper/2505.21574/) (2025)
- [Improving Knowledge Distillation Under Unknown Covariate Shift Through Confidence-Guided Data Augmentation](https://mlsanity.com/paper/2506.02294/) (2025)
- [On the Difficulty of Learning a Meta-network for Training Data Selection](https://mlsanity.com/paper/2606.00571/) (2026)
- [S2-FracMix: Label-Preserving Self-Saliency Mixup Augmentation](https://mlsanity.com/paper/2606.25784/) (2026)
- [Mixup Regularization: A Probabilistic Perspective](https://mlsanity.com/paper/2502.13825/) (2025)
- [Enhancing Generalization in Data-free Quantization via Mixup-class Prompting](https://mlsanity.com/paper/2507.21947/) (2025)
- [InstructMixup: Instruction-Guided Salient Patch Editing for Robust Data Augmentation](https://mlsanity.com/paper/2607.19324/) (2026)
- [NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models](https://mlsanity.com/paper/2509.00378/) (2025)
- [Data Augmentation via Latent Diffusion Models for Detecting Smell-Related Objects in Historical Artworks](https://mlsanity.com/paper/2509.14755/) (2025)
- [GenView++: Unifying Adaptive Generative Augmentation and Quality-Driven Supervision for Contrastive Representation Learning](https://mlsanity.com/paper/2509.23770/) (2025)
- [Diffusion-Classifier Synergy: Reward-Aligned Learning via Mutual Boosting Loop for FSCIL](https://mlsanity.com/paper/2510.03608/) (2025)
- [Salient Concept-Aware Generative Data Augmentation](https://mlsanity.com/paper/2510.15194/) (2025)
- [Beyond Objects: Contextual Synthetic Data Generation for Fine-Grained Classification](https://mlsanity.com/paper/2510.24078/) (2025)
- [SASG-DA: Sparse-Aware Semantic-Guided Diffusion Augmentation For Myoelectric Gesture Recognition](https://mlsanity.com/paper/2511.08344/) (2025)
- [BLENDER: Blended Text Embeddings and Diffusion Residuals for Intra-Class Image Synthesis in Deep Metric Learning](https://mlsanity.com/paper/2601.20246/) (2026)
- [Diffusion-Based Data Augmentation for Image Recognition: A Systematic Analysis and Evaluation](https://mlsanity.com/paper/2603.08364/) (2026)

## Abstract (arXiv)

Text-to-image (T2I) generative models have recently emerged as a powerful tool, enabling the creation of photo-realistic images and giving rise to a multitude of applications. However, the effective integration of T2I models into fundamental image classification tasks remains an open question. A prevalent strategy to bolster image classification performance is through augmenting the training set with synthetic images generated by T2I models. In this study, we scrutinize the shortcomings of both current generative and conventional data augmentation techniques. Our analysis reveals that these methods struggle to produce images that are both faithful (in terms of foreground objects) and diverse (in terms of background contexts) for domain-specific concepts. To tackle this challenge, we introduce an innovative inter-class data augmentation method known as Diff-Mix (https://github.com/Zhicaiwww/Diff-Mix), which enriches the dataset by performing image translations between classes. Our empirical results demonstrate that Diff-Mix achieves a better balance between faithfulness and diversity, leading to a marked improvement in performance across diverse image classification scenarios, including few-shot, conventional, and long-tail classifications for domain-specific datasets.