# Diffusion-based Neural Network Weights Generation

**Authors:** Bedionita Soro, Bruno Andreis, Hayeon Lee, Song Chong, Frank Hutter, Sung Ju Hwang  
**arXiv:** [2402.18153](https://arxiv.org/abs/2402.18153)  
**Date:** 2024-02-28  
**Summary page:** https://www.summarizepaper.com/en/paper/2402.18153v1/

## Key points

- Transfer learning is crucial in deep learning research for faster convergence and improved performance on new tasks
- Effectiveness of transfer learning depends on similarity between source and target datasets
- Common practice of blindly selecting pretrained models can be costly
- Proposed approach introduces efficient and adaptive transfer learning scheme through dataset-conditioned pretrained weights sampling
- Utilizes latent diffusion model combined with variational autoencoder to reconstruct neural network weights and learn distribution of pretrained weights conditioned on each dataset

## Summary

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Transfer learning is a crucial topic in deep learning research, as it allows for faster convergence and improved performance on new tasks. However, the effectiveness of transfer learning heavily relies on the similarity between the source and target datasets. Training a model on numerous datasets can be costly, leading to the common practice of blindly selecting pretrained models with hopes of achieving good performance on a given task. To address these limitations, a novel approach has been proposed that introduces an efficient and adaptive transfer learning scheme through dataset-conditioned pretrained weights sampling. This approach utilizes a latent diffusion model combined with a variational autoencoder to reconstruct neural network weights and learn the distribution of pretrained weights conditioned on each dataset. The key idea behind this approach is to leverage the learned distribution of neural network weights from various pretrained models to enable adaptive sampling for unseen datasets. By training the diffusion model on a diverse set of pretrained weights, this method facilitates faster convergence and competitive performance when applied to new datasets. In summary, this innovative approach aims to enhance transfer learning by utilizing dataset-conditioned pretrained weights sampling through a latent diffusion model. By learning from a variety of pretrained models, this method enables adaptive weight sampling for unseen datasets, ultimately improving convergence speed and overall performance compared to traditional approaches.