Few-shot Image Generation with Elastic Weight Consolidation (2012.02780v1)

Abstract: Few-shot image generation seeks to generate more data of a given domain, with only few available training examples. As it is unreasonable to expect to fully infer the distribution from just a few observations (e.g., emojis), we seek to leverage a large, related source domain as pretraining (e.g., human faces). Thus, we wish to preserve the diversity of the source domain, while adapting to the appearance of the target. We adapt a pretrained model, without introducing any additional parameters, to the few examples of the target domain. Crucially, we regularize the changes of the weights during this adaptation, in order to best preserve the information of the source dataset, while fitting the target. We demonstrate the effectiveness of our algorithm by generating high-quality results of different target domains, including those with extremely few examples (e.g., <10). We also analyze the performance of our method with respect to some important factors, such as the number of examples and the dissimilarity between the source and target domain.

• The paper introduces a novel strategy to adapt pre-trained GANs to few-shot domains using EWC to preserve critical weights and maintain source diversity.
• It demonstrates that high-quality, diverse image generation is possible with extremely limited examples (≤10), outperforming alternative methods on FID and LPIPS metrics.
• The approach mitigates catastrophic forgetting through Fisher Information, offering practical implications for creative applications and data-scarce scenarios.

Few-shot Image Generation with Elastic Weight Consolidation

The paper "Few-shot Image Generation with Elastic Weight Consolidation" presents an innovative approach for generating images from domains with limited available data, leveraging the concept of few-shot learning in generative tasks. The authors propose a methodology to adapt pre-trained generative models to new target domains, using Elastic Weight Consolidation (EWC) to regularize weight changes and maintain diversity from the source domain during adaptation. This work provides critical insights into how generative models can be effectively adapted with minimal data, offering a shared latent factor between source and target domains.

Few-shot learning traditionally focuses on discriminative tasks, while this paper extends the approach to generative models, such as GANs. The challenge lies in adapting the weights of a pre-trained model with few examples from the target domain, without adding new parameters, thereby avoiding cumbersome manual modifications. The EWC method mitigates catastrophic forgetting by preserving critical weight parameters from the source domain using Fisher Information, a proxy objective that evaluates the importance of each parameter during adaptation.

Key Contributions

• The paper introduces an adaptation strategy for pre-trained generative models to few-shot domains without introducing additional parameters, enhancing diversity while preventing overfitting.
• Experimental results demonstrate the effectiveness of this approach, with high-quality adaptations even when extremely few examples ($\leq$10) are available.
• Detailed analysis and comparison of this method against other techniques, like NST, BSA, and MineGAN, highlight its superiority in generating realistic and diverse outputs.

Experimental Analysis

Quantitative and qualitative evaluations reveal that the proposed method achieves superior results in terms of Fréchet Inception Distance (FID) and LPIPS metrics, indicating higher image quality and diversity compared to alternative approaches. Moreover, user studies, conducted via Amazon Mechanical Turk, confirm that images generated using this method are more likely to be perceived as real, reinforcing the realism of its outputs.

Implications and Future Work

The findings from this paper are particularly relevant for domains with scarce data, such as artistic or stylized content generation, offering practical applications in digital creativity and data expansion. The approach mitigates the need for large datasets, consequently reducing cost and effort while enabling enhanced training of AI models in data-constrained scenarios.

This methodology could inspire further exploration of few-shot learning in other generative domains, potentially leading to broader applications in unsupervised learning and continuous learning frameworks. Future research might explore hybrid strategies that integrate few-shot style transfer techniques with generative models and investigate cross-domain versatility beyond visual content, such as audio or textual data generation.

By offering a robust adaptation strategy that prevents overfitting with low data, this paper contributes significantly to the field of generative modeling, providing a foundation for advancements in AI-driven creative processes.