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

TL;DR

This work investigates integrating foundation models into Transformer-based hypernetworks to improve generalizable implicit neural representations (INRs). By augmenting a Trans-INR–like framework with pre-trained foundation-model encoders and prompting-based fine-tuning, the authors demonstrate across novel view synthesis and audio reconstruction that foundation models enhance performance, generalization to unseen data, and data efficiency, even under parameter-efficient settings. Key findings show that larger foundation models, fine-tuning vs freezing tradeoffs, and prompt-based approaches affect outcomes, with CLIP, DINO, and DINOv2 often delivering the strongest gains and MAE underperforming due to weaker global representations. The study also analyzes the design space (choice of foundation model, algorithms, and scaling) and validates robustness across modalities, suggesting a practical blueprint for deploying foundation-model–augmented hypernetworks in real-world INR tasks.

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.

Figures (4)

• Figure 1: An overview of the hypernetwork-foundation model framework. First, an image is tokenized and concatenated with learnable weight tokens. Second, all tokens are encoded by a pre-trained foundation model encoder (Eq. \ref{['eqn:encoder']}). Tokens are then grouped, transformed using linear heads $\texttt{Head}_k$, and multiplied element-wise $\otimes$ with the base parameter $\texttt{BaseParam}_k$. (Eq. \ref{['eqn:heads']}), and normalized (not shown). The resulting masked weights are then used to instantiate an implicit neural representation (INR). The INR can then be trained as usual.
• Figure 2: Plots showing performance vs the amount of training data for both the randomly initialized (Random) and foundation model (FM) strategies.
• Figure 3: Plots of NVS performance vs number of Transformer encoder parameters, as measured by the four metrics, on the NVS task using the Trans-INR algorithm. We find that increasing model size generally leads to increased performance, with supervised ViTs dosovitskiy2020image being a clear outlier.
• Figure 4: Comparison of qualitative results between the best foundation model-based hypernetwork and hypernetworks trained from scratch. Novel views generated with the hypernetwork approach (FM) are more faithful to the groundtruth than the baseline (Random). For example, the lamp in the middle row is better reconstructed at both the top of the lamp and on its stem, while for the two chairs the FM approach better captures their curved backs. You may need to zoom in to see the differences.