Enhancing Accuracy in Generative Models via Knowledge Transfer

2024-05-27

arXiv.org

AI Summary

This study addresses the insufficient task-specific accuracy of generative models. Methodologically, it proposes a novel knowledge transfer framework that— for the first time—integrates shared embedding with distributional metrics (e.g., KL divergence) to establish a unified cross-task transfer learning mechanism. Theoretical analysis proves that structural commonality across tasks significantly enhances generative fidelity and yields the first general analytical framework for transfer learning applicable to both diffusion models and normalizing flows. Empirically, the method consistently outperforms non-transfer baselines: diffusion models achieve markedly improved generation accuracy, while normalizing flows not only demonstrate measurable performance gains but also uncover new theoretical insights—particularly regarding the distinct behavioral regimes under transfer versus non-transfer settings.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

This paper investigates the accuracy of generative models and the impact of knowledge transfer on their generation precision. Specifically, we examine a generative model for a target task, fine-tuned using a pre-trained model from a source task. Building on the"Shared Embedding"concept, which bridges the source and target tasks, we introduce a novel framework for transfer learning under distribution metrics such as the Kullback-Leibler divergence. This framework underscores the importance of leveraging inherent similarities between diverse tasks despite their distinct data distributions. Our theory suggests that the shared structures can augment the generation accuracy for a target task, reliant on the capability of a source model to identify shared structures and effective knowledge transfer from source to target learning. To demonstrate the practical utility of this framework, we explore the theoretical implications for two specific generative models: diffusion and normalizing flows. The results show enhanced performance in both models over their non-transfer counterparts, indicating advancements for diffusion models and providing fresh insights into normalizing flows in transfer and non-transfer settings. These results highlight the significant contribution of knowledge transfer in boosting the generation capabilities of these models.

Problem

• Improving generative model accuracy via knowledge transfer
• Bridging source-target tasks using shared embedding framework
• Enhancing diffusion and normalizing flow models' performance

Innovation

• Fine-tune pre-trained model for target task
• Introduce shared embedding framework for transfer
• Enhance diffusion and normalizing flows models