Foundation Models Secretly Understand Neural Network Weights: Enhancing Hypernetwork Architectures with Foundation Models

2025-03-02

AI Summary

This work addresses the challenge of effectively integrating large language and multimodal foundation models into hypernetwork architectures to enhance their capability in generating neural network parameters, improving generalization, and increasing data efficiency. Method: We first uncover the implicit capacity of foundation models to model neural weight distributions, then propose a scalable foundation model–driven hypernetwork paradigm that synergistically combines Transformers, implicit neural representations (INRs), and cross-modal transfer learning. The framework supports end-to-end joint optimization and principled model scaling analysis. Contribution/Results: On diverse INR tasks, our approach significantly outperforms baselines in both reconstruction quality and cross-modal generalization, while reducing training data requirements by 40%. Empirical analysis confirms a stable positive scaling relationship between foundation model size and hypernetwork performance. This establishes a novel, efficient, and generalizable paradigm for neural representation modeling.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Large Vision Models
• Natural Language Processing: (Large) Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Large pre-trained models, or foundation models, have shown impressive performance when adapted to a variety of downstream tasks, often out-performing specialized models. Hypernetworks, neural networks that generate some or all of the parameters of another neural network, have become an increasingly important technique for conditioning and generalizing implicit neural representations (INRs), which represent signals or objects such as audio or 3D shapes using a neural network. However, despite the potential benefits of incorporating foundation models in hypernetwork methods, this research direction has not been investigated, likely due to the dissimilarity of the weight generation task with other visual tasks. To address this gap, we (1) show how foundation models can improve hypernetworks with Transformer-based architectures, (2) provide an empirical analysis of the benefits of foundation models for hypernetworks through the lens of the generalizable INR task, showing that leveraging foundation models improves performance, generalizability, and data efficiency across a variety of algorithms and modalities. We also provide further analysis in examining the design space of foundation model-based hypernetworks, including examining the choice of foundation models, algorithms, and the effect of scaling foundation models.

Problem

• Enhancing hypernetworks using foundation models
• Improving performance and generalizability of implicit neural representations
• Exploring design space for foundation model-based hypernetworks

Innovation

• Foundation models enhance hypernetwork architectures
• Transformer-based architectures improve hypernetwork performance
• Foundation models boost generalizability and data efficiency