Few-shot Image Generation via Information Transfer from the Built Geodesic Surface

• URL: http://arxiv.org/abs/2401.01749v2
• Date: Sun, 3 Mar 2024 03:00:33 GMT
• Title: Few-shot Image Generation via Information Transfer from the Built Geodesic Surface
• Authors: Yuexing Han and Liheng Ruan and Bing Wang
• Abstract summary: We propose a method called Information Transfer from the Built Geodesic Surface (ITBGS) With the FAGS module, a pseudo-source domain is created by projecting image features from the training dataset into the Pre-Shape Space. We demonstrate that the proposed method consistently achieves optimal or comparable results across a diverse range of semantically distinct datasets.
• Score: 2.617962830559083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Images generated by most of generative models trained with limited data often exhibit deficiencies in either fidelity, diversity, or both. One effective solution to address the limitation is few-shot generative model adaption. However, the type of approaches typically rely on a large-scale pre-trained model, serving as a source domain, to facilitate information transfer to the target domain. In this paper, we propose a method called Information Transfer from the Built Geodesic Surface (ITBGS), which contains two module: Feature Augmentation on Geodesic Surface (FAGS); Interpolation and Regularization (I\&R). With the FAGS module, a pseudo-source domain is created by projecting image features from the training dataset into the Pre-Shape Space, subsequently generating new features on the Geodesic surface. Thus, no pre-trained models is needed for the adaption process during the training of generative models with FAGS. I\&R module are introduced for supervising the interpolated images and regularizing their relative distances, respectively, to further enhance the quality of generated images. Through qualitative and quantitative experiments, we demonstrate that the proposed method consistently achieves optimal or comparable results across a diverse range of semantically distinct datasets, even in extremely few-shot scenarios.

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