# F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation

arXiv: https://arxiv.org/abs/2008.01999 · HTML: https://mlsanity.com/paper/2008.01999/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 29 of 29)

- [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)
- [A Comprehensive Survey on Data-Efficient GANs in Image Generation](https://mlsanity.com/paper/2204.08329/) (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)
- [LFS-GAN: Lifelong Few-Shot Image Generation](https://mlsanity.com/paper/2308.11917/) (2023)
- [Image Synthesis under Limited Data: A Survey and Taxonomy](https://mlsanity.com/paper/2307.16879/) (2023)
- [Generating Novel Scene Compositions from Single Images and Videos](https://mlsanity.com/paper/2103.13389/) (2021)
- [Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical Deformation](https://mlsanity.com/paper/2308.10898/) (2023)
- [Adaptive Feature Interpolation for Low-Shot Image Generation](https://mlsanity.com/paper/2112.02450/) (2021)
- [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)
- [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)
- [MSN: Multi-directional Similarity Network for Hand-crafted and Deep-synthesized Copy-Move Forgery Detection](https://mlsanity.com/paper/2512.07110/) (2025)
- [Consistency-Regularized GAN for Few-Shot SAR Target Recognition](https://mlsanity.com/paper/2601.15681/) (2026)

## Abstract (arXiv)

In order to generate images for a given category, existing deep generative models generally rely on abundant training images. However, extensive data acquisition is expensive and fast learning ability from limited data is necessarily required in real-world applications. Also, these existing methods are not well-suited for fast adaptation to a new category. Few-shot image generation, aiming to generate images from only a few images for a new category, has attracted some research interest. In this paper, we propose a Fusing-and-Filling Generative Adversarial Network (F2GAN) to generate realistic and diverse images for a new category with only a few images. In our F2GAN, a fusion generator is designed to fuse the high-level features of conditional images with random interpolation coefficients, and then fills in attended low-level details with non-local attention module to produce a new image. Moreover, our discriminator can ensure the diversity of generated images by a mode seeking loss and an interpolation regression loss. Extensive experiments on five datasets demonstrate the effectiveness of our proposed method for few-shot image generation.