Keywords: GANs, few-shot image generation, meta-learning, Transformer GANs
Abstract: While Generative Adversarial Networks (GANs) have rapidly advanced the state of the art in deep generative modeling, they require a large amount of diverse datapoints to adequately train, limiting their potential in domains where data is constrained. In this study, we explore the potential of few-shot image generation, enabling GANs to rapidly adapt to a small support set of datapoints from an unseen target domain and generate novel, high-quality examples from that domain. To do so, we adapt two common meta-learning algorithms from few-shot classification--Model-Agnostic Meta-Learning (MAML) and Reptile--to GANs, meta-training the generator and discriminator to learn an optimal weight initialization such that fine-tuning on a new task is rapid. Empirically, we demonstrate how our MAML and Reptile meta-learning algorithms, meta-trained on tasks from the MNIST and SVHN datasets, rapidly adapt at test time to unseen tasks and generate high-quality, photorealistic samples from these domains given only tens of support examples. In fact, we show that the generated image quality of these few-shot adapted models is on par with that of a baseline model vanilla-trained on thousands of samples from the same domain. Intriguingly, meta-training also takes substantially less time to converge compared to baseline training, indicating the power and efficiency of our approach. We also demonstrate the generalizability of our algorithms, working with both CNN- and Transformer-parametrized GANs. Overall, we present our MAML and Reptile meta-learning algorithms as effective strategies to enable few-shot image generation, improving the feasibility of deep generative models in practice.