mlsanityData to 14 Oct 2026

MatchingGAN: Matching-based Few-shot Image Generation

arXiv 2003.03497 ↗First posted 7 Mar 2020cs.CV cs.LG eess.IVcited by 26

Not on the map. Why?

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.

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

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 7; 2023: 9; 2024: 6; 2025: 0; 2026: 1.

26 papers.

Sort:

By area:

  1. 2022WaveGAN: Frequency-aware GAN for High-Fidelity Few-shot Image Generationcited by 22
  2. 2022The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image Generationcited by 22
  3. 2021Learning Prototype-oriented Set Representations for Meta-Learningcited by 19
  4. 2023A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shotcited by 19
  5. 2021Few-shot Image Generation with Mixup-based Distance Learningcited by 16
  6. 2022Attribute Group Editing for Reliable Few-shot Image Generationcited by 15
  7. 2023WeditGAN: Few-Shot Image Generation via Latent Space Relocationcited by 12
  8. 2023Image Synthesis under Limited Data: A Survey and Taxonomycited by 9
  9. 2023Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical Deformationcited by 8
  10. 2022Few-shot Image Generation Using Discrete Content Representationcited by 6
  11. 2023Stable Attribute Group Editing for Reliable Few-shot Image Generationcited by 6
  12. 2024HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generationcited by 4
14 more
  1. 2023Cross-modulated Few-shot Image Generation for Colorectal Tissue Classificationcited by 3
  2. 2024ComFusion: Personalized Subject Generation in Multiple Specific Scenes From Single Imagecited by 2
  3. 2022Overcoming challenges in leveraging GANs for few-shot data augmentationcited by 1
  4. 2022D3T-GAN: Data-Dependent Domain Transfer GANs for Few-shot Image Generationcited by 1
  5. 2023DEff-GAN: Diverse Attribute Transfer for Few-Shot Image Synthesiscited by 1
  6. 2024Conditional Distribution Modelling for Few-Shot Image Synthesis with Diffusion Modelscited by 1
  7. 2021Dual Path Structural Contrastive Embeddings for Learning Novel Objectscited by 0
  8. 2022DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Deltacited by 0
  9. 2023EqGAN: Feature Equalization Fusion for Few-shot Image Generationcited by 0
  10. 2023Improving Few-shot Image Generation by Structural Discrimination and Textural Modulationcited by 0
  11. 2024Active Generation Network of Human Skeleton for Action Recognitioncited by 0
  12. 2024TAGE: Trustworthy Attribute Group Editing for Stable Few-shot Image Generationcited by 0
  13. 2024A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Seriescited by 0
  14. 2026Consistency-Regularized GAN for Few-Shot SAR Target Recognitioncited by 0