# MatchingGAN: Matching-based Few-shot Image Generation

arXiv: https://arxiv.org/abs/2003.03497 · HTML: https://mlsanity.com/paper/2003.03497/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2020.

## Built on by (most cited first, 26 of 26)

- [WaveGAN: Frequency-aware GAN for High-Fidelity Few-shot Image Generation](https://mlsanity.com/paper/2207.07288/) (2022)
- [The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image Generation](https://mlsanity.com/paper/2211.12347/) (2022)
- [Learning Prototype-oriented Set Representations for Meta-Learning](https://mlsanity.com/paper/2110.09140/) (2021)
- [A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot](https://mlsanity.com/paper/2307.14397/) (2023)
- [Few-shot Image Generation with Mixup-based Distance Learning](https://mlsanity.com/paper/2111.11672/) (2021)
- [Attribute Group Editing for Reliable Few-shot Image Generation](https://mlsanity.com/paper/2203.08422/) (2022)
- [WeditGAN: Few-Shot Image Generation via Latent Space Relocation](https://mlsanity.com/paper/2305.06671/) (2023)
- [Image Synthesis under Limited Data: A Survey and Taxonomy](https://mlsanity.com/paper/2307.16879/) (2023)
- [Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical Deformation](https://mlsanity.com/paper/2308.10898/) (2023)
- [Few-shot Image Generation Using Discrete Content Representation](https://mlsanity.com/paper/2207.10833/) (2022)
- [Stable Attribute Group Editing for Reliable Few-shot Image Generation](https://mlsanity.com/paper/2302.00179/) (2023)
- [HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation](https://mlsanity.com/paper/2411.17784/) (2024)
- [Cross-modulated Few-shot Image Generation for Colorectal Tissue Classification](https://mlsanity.com/paper/2304.01992/) (2023)
- [ComFusion: Personalized Subject Generation in Multiple Specific Scenes From Single Image](https://mlsanity.com/paper/2402.11849/) (2024)
- [Overcoming challenges in leveraging GANs for few-shot data augmentation](https://mlsanity.com/paper/2203.16662/) (2022)
- [D3T-GAN: Data-Dependent Domain Transfer GANs for Few-shot Image Generation](https://mlsanity.com/paper/2205.06032/) (2022)
- [DEff-GAN: Diverse Attribute Transfer for Few-Shot Image Synthesis](https://mlsanity.com/paper/2302.14533/) (2023)
- [Conditional Distribution Modelling for Few-Shot Image Synthesis with Diffusion Models](https://mlsanity.com/paper/2404.16556/) (2024)
- [Dual Path Structural Contrastive Embeddings for Learning Novel Objects](https://mlsanity.com/paper/2112.12359/) (2021)
- [DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta](https://mlsanity.com/paper/2207.10271/) (2022)
- [EqGAN: Feature Equalization Fusion for Few-shot Image Generation](https://mlsanity.com/paper/2307.14638/) (2023)
- [Improving Few-shot Image Generation by Structural Discrimination and Textural Modulation](https://mlsanity.com/paper/2308.16110/) (2023)
- [Active Generation Network of Human Skeleton for Action Recognition](https://mlsanity.com/paper/2401.17086/) (2024)
- [TAGE: Trustworthy Attribute Group Editing for Stable Few-shot Image Generation](https://mlsanity.com/paper/2410.17855/) (2024)
- [A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series](https://mlsanity.com/paper/2412.00772/) (2024)
- [Consistency-Regularized GAN for Few-Shot SAR Target Recognition](https://mlsanity.com/paper/2601.15681/) (2026)

## Abstract (arXiv)

To generate new images for a given category, most deep generative models require abundant training images from this category, which are often too expensive to acquire. To achieve the goal of generation based on only a few images, we propose matching-based Generative Adversarial Network (GAN) for few-shot generation, which includes a matching generator and a matching discriminator. Matching generator can match random vectors with a few conditional images from the same category and generate new images for this category based on the fused features. The matching discriminator extends conventional GAN discriminator by matching the feature of generated image with the fused feature of conditional images. Extensive experiments on three datasets demonstrate the effectiveness of our proposed method.