D3T-GAN: Data-Dependent Domain Transfer GANs for Few-shot Image Generation

Abstract

As an important and challenging problem, few-shot image generation aims at generating realistic images through training a GAN model given few samples. A typical solution for few-shot generation is to transfer a well-trained GAN model from a data-rich source domain to the data-deficient target domain. In this paper, we propose a novel self-supervised transfer scheme termed D 3 T-GAN, addressing the cross-domain GANs transfer in few-shot image generation. Specifically, we design two individual strategies to transfer knowledge between generators and discriminators, respectively. To transfer knowledge between generators, we conduct a data-dependent transformation, which projects and reconstructs the target samples into the source generator space. Then, we perform knowledge transfer from transformed samples to generated samples. To transfer knowledge between discriminators, we design a multi-level discriminant knowledge distillation from the source discriminator to the target discriminator on both the real and fake samples. Extensive experiments show that our method improve the quality of generated images and achieves the state-of-the-art FID scores on commonly used datasets.

A nonlinear anisotropic diffusion model with forward-backward diffusivities for image denoising

“…These include scale and shift parameters [32], updating only the higher discriminator layers [28], linear combinations of scale and shift parameters [41], modulating kernels or convolutions [59,58,10,2] and singular values [38], mapping networks from noise to latents [46,29,53] and latent offsets [12]. Various works apply regularization losses by enforcing constraints to samples/weights by the source generator including elastic weight regularization [27], domain correspondence [33,16,22], contrastive learning [60], spatial alignment [51], inversion [49,23,44], random masks on discriminators [61] and alignment free spatial correlation [30]. Given the increasing popularity of VQ-VAE and diffusion based models, recent work [43] and [61] explore few-shot finetuning on VQ-VAE tokens and diffusion models.…”