F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation
This addresses the need for fast adaptation to new categories in image generation, reducing data acquisition costs, though it is incremental as it builds on existing GAN frameworks.
The paper tackles the problem of generating realistic and diverse images for new categories with only a few training examples, proposing F2GAN which achieves effective few-shot image generation as demonstrated on five datasets.
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.