Few-Shot Generative Model Adaption via Identity Injection and Preservation

2026-03-24

AI Summary

This work addresses the challenge of catastrophic forgetting of source-domain identity knowledge when fine-tuning generative models under extreme few-shot conditions (fewer than 10 images), which often leads to degraded generation quality. To mitigate this issue, the authors propose a novel approach that integrates identity injection with feature consistency alignment. The method employs an identity injection module, an identity replacement mechanism, style-content disentanglement, and reconstruction modulation to effectively preserve source-domain identity information during target-domain adaptation. Extensive experiments on multiple public datasets demonstrate that the proposed method significantly outperforms current state-of-the-art techniques, achieving consistent improvements across five evaluation metrics and simultaneously enhancing both identity fidelity and overall visual quality of the generated images.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Training generative models with limited data presents severe challenges of mode collapse. A common approach is to adapt a large pretrained generative model upon a target domain with very few samples (fewer than 10), known as few-shot generative model adaptation. However, existing methods often suffer from forgetting source domain identity knowledge during adaptation, which degrades the quality of generated images in the target domain. To address this, we propose Identity Injection and Preservation (I$^2$P), which leverages identity injection and consistency alignment to preserve the source identity knowledge. Specifically, we first introduce an identity injection module that integrates source domain identity knowledge into the target domain's latent space, ensuring the generated images retain key identity knowledge of the source domain. Second, we design an identity substitution module, which includes a style-content decoupler and a reconstruction modulator, to further enhance source domain identity preservation. We enforce identity consistency constraints by aligning features from identity substitution, thereby preserving identity knowledge. Both quantitative and qualitative experiments show that our method achieves substantial improvements over state-of-the-art methods on multiple public datasets and 5 metrics.

Problem

• Few-Shot Generative Model Adaptation
• Mode Collapse
• Identity Preservation
• Source Domain Knowledge
• Limited Data

Innovation

• Few-shot adaptation
• Generative model
• Identity preservation
• Latent space injection
• Style-content disentanglement