Neural Network Diffusion: Generating High-Performing Neural Network Parameters

Despite the great success of diffusion models in visual generation, their potential in other domains still needs to be explored. Existing research methodologies have demonstrated the remarkable efficacy of diffusion models in generating high-quality images and videos. However, their application beyond visual domains still needs to be explored. Through empirical studies employing techniques such as comparative analysis and experimental validation, researchers can explore the potential of diffusion models in diverse domains. 

Diffusion models, rooted in non-equilibrium thermodynamics, initially aimed to denoise images. Refinements like DDPM and DDIM enhanced training with forward and reverse processes. GuidedDiffusion improved model architecture, surpassing GAN-based methods. Subsequent works—GLIDE, Imagen, DALL·E 2, and Stable Diffusion achieve photorealistic images adopted by artists. Yet, the diffusion model’s potential in non-visual domains remains underexplored. Parameter generation, distinct from visual generation, aims to create neural network parameters for task performance. While prior work explores stochastic and Bayesian methods, applying diffusion models in parameter generation remains underexplored.

Researchers from the National University of Singapore, University of California, Berkeley, and Meta AI Research have proposed neural network diffusion, a novel approach to parameter generation. Leveraging a standard latent diffusion model and an autoencoder, p-diff synthesizes new high-performing parameters. By training the autoencoder to extract latent representations and utilizing the diffusion model to transform random noise, p-diff generates parameters that consistently match or surpass the performance of models introduced by the SGD optimizer. This approach ensures diversity in generated parameters while maintaining high performance across various datasets and architectures, offering potential applications beyond traditional domains.

Neural network diffusion comprises two main processes: parameter autoencoder and generation. In the parameter autoencoder process, a subset of high-performing model parameters is flattened into 1-dimensional vectors and fed into an autoencoder for latent representation extraction and reconstruction. The generation process utilizes a standard latent diffusion model trained on random noise to generate new parameters via a reverse process, leveraging a denoising network and a trained decoder.

Across eight datasets and six architectures, Neural network diffusion demonstrates competitive or superior performance compared to baselines. Results indicate efficient learning of high-performing parameter distributions and effective generation of superior models from random noise. The method consistently achieves strong performance across diverse datasets and architectures. These findings underscore the method’s capability to generate high-performing parameters across various scenarios, showcasing its robustness and effectiveness. 

In summary, diffusion models can generate high-performing and novel neural network parameters, demonstrating their superiority. Using diffusion steps for neural network parameter updates shows a potentially novel paradigm in deep learning. However, it is evident that images/videos and parameters are signals of different natures, and this distinction must be handled with care. Although diffusion models have achieved considerable success in image/video generation, their application to parameters still needs to be explored. These pose a series of challenges for neural network diffusion.

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