Generative Modeling of Weights: Generalization or Memorization? (2506.07998v1)

Abstract: Generative models, with their success in image and video generation, have recently been explored for synthesizing effective neural network weights. These approaches take trained neural network checkpoints as training data, and aim to generate high-performing neural network weights during inference. In this work, we examine four representative methods on their ability to generate novel model weights, i.e., weights that are different from the checkpoints seen during training. Surprisingly, we find that these methods synthesize weights largely by memorization: they produce either replicas, or at best simple interpolations, of the training checkpoints. Current methods fail to outperform simple baselines, such as adding noise to the weights or taking a simple weight ensemble, in obtaining different and simultaneously high-performing models. We further show that this memorization cannot be effectively mitigated by modifying modeling factors commonly associated with memorization in image diffusion models, or applying data augmentations. Our findings provide a realistic assessment of what types of data current generative models can model, and highlight the need for more careful evaluation of generative models in new domains. Our code is available at https://github.com/boyazeng/weight_memorization.

• The paper finds that generative models primarily replicate or interpolate weights from training data rather than producing novel architectures.
• It demonstrates that synthesized weights closely mirror training checkpoints, replicating key decision boundaries and structural patterns.
• It reveals that common augmentation and regularization strategies fail to curb memorization, highlighting the need for innovative modeling approaches.

Generative Modeling of Neural Network Weights – Generalization or Memorization?

This paper undertakes an empirical investigation into generative models designed to synthesize neural network weights, examining their capability to produce novel weights against mere replication of training data. The central premise revolves around leveraging generative techniques, akin to those applied in image synthesis, to generate high-performing neural network checkpoints without direct access to the original training data.

Major Findings

1. Memorization Over Novelty: Across four distinct generative methods, the paper reveals a predominant trend where generated neural network weights are near-replicas or interpolations of the training checkpoints. Techniques including Hyper-Representations, G.pt, HyperDiffusion, and P-diff fail to outperform baseline methods like noise addition or simple weight ensembles in creating distinct yet high-quality models.
2. Analysis of Generated Model Behavior: Models produced via these generative methods demonstrate significant behavioral similarities with their nearest training checkpoints. This assessment spans decision boundaries in classification models and reconstructed shapes in neural field models, indicating a lack of genuine novelty in function generation.
3. Mitigation Attempts: Traditional modeling factors and strategies effective in mitigating memorization in image diffusion models, such as model size variation and regularization changes, are ineffective for generative models of weights. Augmentation strategies exploiting symmetry (e.g., permutation of weights) do not lead to substantial reductions in memorization either.
4. Intrinsic Complexity of Weight Data: The paper provides a theoretical angle, suggesting higher intrinsic dimensions of weight data compared to image data, potentially explaining the difficulty in achieving generalization akin to image generative models.

Implications and Future Directions

This examination reveals crucial insights into the limitations of current generative models of weights. The findings underscore the necessity for more sophisticated evaluative criteria beyond standard performance metrics, specifically targeting memorization aspects to ensure the generation of genuinely novel and varied outputs. Ensuring models do not duplicate training data is especially vital for applications involving sensitive or private datasets.

The inadequacy of straightforward data augmentation and parameter tuning indicates potential avenues for future research:

• Architectural Innovations: Incorporating explicit design features to capture the unique symmetries and dependencies inherent in weight data might enhance generalization.
• Alternative Modeling Frameworks: Exploring non-traditional generative modeling approaches could potentially yield better results, avoiding entrenched issues observed in stochastic models.
• Cross-domain Learning: Leveraging insights from successful cross-modal generative applications (e.g., text-to-image generation) could refine weight synthesis models.

Conclusion

The paper provides a detailed exploration into the memorization tendencies of generative models for weights, contrasting their replication capabilities against expected generative functionalities. By shining a spotlight on potential weaknesses in existing approaches, it paves the way for renewed efforts and novel techniques aimed at realizing the full potential of generative weight modeling, with implications extending across computational design and machine learning disciplines.