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Foundation Models Secretly Understand Neural Network Weights: Enhancing Hypernetwork Architectures with Foundation Models
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Shows that initializing Transformer-based INR hypernetworks (Trans-INR, PONP, IPC) with pre-trained foundation models, either fully fine-tuned or frozen with prompt tuning, improves novel view synthesis and audio reconstruction performance, generalization, and data efficiency.
Problem
Whether and how foundation models can improve hypernetworks that generate implicit neural representations (INRs), given the gap between neural network weight generation and typical vision tasks.
Approach
Replace the from-scratch Transformer encoder of Trans-INR-style hypernetworks with a pre-trained foundation model encoder (ViT, DINO, DINOv2, CLIP, etc.), keeping learnable weight tokens and per-layer linear heads that modulate base INR parameters; study full fine-tuning, frozen (prompt-tuned) and LoRA variants.
Key results
- Fine-tuned DINOv2 backbone in Trans-INR reaches 22.095 PSNR on NVS vs 20.862 for random initialization, the best of the backbones tested.
- Frozen DINO with about 100K trainable parameters reaches 21.035 PSNR, above random initialization (20.862) with 87.3M trainable parameters, though worse on LPIPS and FID.
- Fine-tuned Trans-INR, PONP, and IPC all improve over their random-initialization versions on NVS, e.g. IPC LPIPS drops from 0.1811 to 0.1450.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Fine-tune the hypernetwork from DINOv2 instead of random initialization | Trans-INR hypernetwork · LearnIt ShapeNet NVS | PSNR: 20.862 -> 22.095 | Helped |
| Fine-tune the hypernetwork from DINOv2 instead of random initialization | Trans-INR hypernetwork · LearnIt ShapeNet NVS | LPIPS: 0.1511 -> 0.1063 | Helped |
| Use an MAE-pretrained backbone instead of random initialization | Trans-INR hypernetwork · LearnIt ShapeNet NVS | PSNR: 20.862 -> 20.701 | Hurt |
| Fine-tune a foundation-model backbone for Trans-INR | Trans-INR · LearnIt ShapeNet NVS | PSNR: 20.850 -> 22.095 | Helped |
| Fine-tune a foundation-model backbone for PONP | PONP · LearnIt ShapeNet NVS | PSNR: 20.878 -> 21.993 | Helped |
| Fine-tune a foundation-model backbone for Instance Pattern Composers | Instance Pattern Composers · LearnIt ShapeNet NVS | PSNR: 20.102 -> 20.672 | Helped |
| Use a fine-tuned foundation-model backbone | Instance Pattern Composers · LearnIt ShapeNet NVS | SSIM: 0.8344 -> 0.8324 | Mixed |
| Freeze DINO instead of random initialization (ablation) | Trans-INR hypernetwork · LearnIt ShapeNet NVS | PSNR: 20.862 -> 21.035 | Helped |
| Freeze DINO instead of fine-tuning all parameters (ablation) | Trans-INR hypernetwork · LearnIt ShapeNet NVS | LPIPS: 0.1101 -> 0.1767 | Hurt |
| Apply LoRA instead of freezing the DINO encoder (ablation) | Trans-INR hypernetwork · LearnIt ShapeNet NVS | PSNR: 21.035 -> 21.246 | Helped |
| Apply LoRA instead of full fine-tuning (ablation) | Trans-INR hypernetwork · LearnIt ShapeNet NVS | PSNR: 21.737 -> 21.246 | Hurt |
| Apply LoRA instead of full fine-tuning (ablation) | Trans-INR hypernetwork · LearnIt ShapeNet NVS | LPIPS: 0.1101 -> 0.1678 | Hurt |
Surprising findings
- MAE, a masked image modeling pre-trained backbone, underperforms random initialization on NVS (20.701 vs 20.862 PSNR), which the authors attribute to poor global image representations.
- Supervised ViT, despite good metrics, is an outlier in the scaling analysis, and better upstream performance does not always transfer downstream.
Limitations
- Does not tokenize task-specific auxiliary information such as camera poses for novel view synthesis.
- Uses a simple volume renderer and simple NeRF; only fine-tuning and freezing (and LoRA in appendix) are investigated.
- Larger datasets such as Objaverse were not investigated.
Experimental setup
Datasets: LearnIt ShapeNet NVS dataset ShapeNet LibriSpeech-clean ImageNet-21k
Benchmarks: ShapeNet NVS (LearnIt) LibriSpeech audio reconstruction
Metrics: PSNR SSIM LPIPS FID
Baselines: Randomly initialized Trans-INR Trans-INR PONP Instance Pattern Composers LearnIt
Builds on: Trans-INR PONP Instance Pattern Composers prompt tuning LoRA
Closest papers by meaning
- HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories (CVPR 2025 · similarity 0.89)
- Specialized Foundation Models Struggle to Beat Supervised Baselines (ICLR 2025 · similarity 0.87)
- Understanding the Differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks (NeurIPS 2024 · similarity 0.87)
- Magnitude Invariant Parametrizations Improve Hypernetwork Learning (ICLR 2024 · similarity 0.87)
- Scaling Laws for Sparsely-Connected Foundation Models (ICLR 2024 · similarity 0.87)
- Towards Scalable and Versatile Weight Space Learning (ICML 2024 · similarity 0.86)
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