Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations

TL;DR

This paper introduces a novel approach to enhance diversity in zero-shot GAN adaptation by leveraging semantic variations in CLIP space, resulting in improved sample diversity and quality without additional training data.

Contribution

It proposes a method to find semantic variations in CLIP space and introduces a directional moment loss, elastic weight consolidation, and relation consistency loss to improve diversity and content preservation.

Findings

Achieves state-of-the-art diversity and quality in zero-shot GAN adaptation.

Effectively maintains source domain content while enhancing diversity.

Demonstrates robustness across various adaptation scenarios.

Abstract

Training deep generative models usually requires a large amount of data. To alleviate the data collection cost, the task of zero-shot GAN adaptation aims to reuse well-trained generators to synthesize images of an unseen target domain without any further training samples. Due to the data absence, the textual description of the target domain and the vision-language models, e.g., CLIP, are utilized to effectively guide the generator. However, with only a single representative text feature instead of real images, the synthesized images gradually lose diversity as the model is optimized, which is also known as mode collapse. To tackle the problem, we propose a novel method to find semantic variations of the target text in the CLIP space. Specifically, we explore diverse semantic variations based on the informative text feature of the target domain while regularizing the uncontrolled deviation of the semantic information. With the obtained variations, we design a novel directional moment loss that matches the first and second moments of image and text direction distributions. Moreover, we introduce elastic weight consolidation and a relation consistency loss to effectively preserve valuable content information from the source domain, e.g., appearances. Through extensive experiments, we demonstrate the efficacy of the proposed methods in ensuring sample diversity in various scenarios of zero-shot GAN adaptation. We also conduct ablation studies to validate the effect of each proposed component. Notably, our model achieves a new state-of-the-art on zero-shot GAN adaptation in terms of both diversity and quality.