
The author proposes an efficient transfer learning scheme using dataset-conditioned pretrained weights sampling to improve convergence and performance on new tasks. By leveraging a latent diffusion model with a variational autoencoder, the approach enables adaptive sampling of weights for unseen datasets.


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Efficient-Neural-Network-Weights-Generation-through-Diffusion-Based-Transfer-Learning


Efficient Neural Network Weights Generation through Diffusion-Based Transfer Learning


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DNN models trained with fixed ETF classifiers improve transfer performance by minimizing class covariances, enhancing cluster separability, and focusing on essential features for class separation.


Enhancing-Transfer-Learning-with-Fixed-ETF-Classifiers-in-DNN-Models


Enhancing Transfer Learning with Fixed ETF Classifiers in DNN Models
