This repository contains the code for paper: Learning to Generate Parameters of ConvNets for Unseen Image Data.
Typical Convolutional Neural Networks (ConvNets) depend heavily on large amounts of image data and resort to an iterative optimization algorithm (e.g., SGD or Adam) to learn network parameters, which makes training very time- and resource-intensive. In this paper, we propose a new training paradigm and formulate the parameter learning of ConvNets into a prediction task: considering that there exists correlations between image datasets and their corresponding optimal network parameters of a given ConvNet, we explore if we can learn a hyper-mapping between them to capture the relations, such that we can directly predict the parameters of the network for an image dataset never seen during the training phase. To do this, we put forward a new hypernetwork based model, called PudNet, which intends to learn a mapping between datasets and their corresponding network parameters, and then predicts parameters for unseen data with only a single forward propagation. Moreover, our model benefits from a series of adaptive hyper recurrent units sharing weights to capture the dependencies of parameters among different network layers. Extensive experiments demonstrate that our proposed method achieves good efficacy for unseen image datasets on two kinds of settings: Intra-dataset prediction and Inter-dataset prediction. Our PudNet can also well scale up to large-scale datasets, e.g., ImageNet-1K._
python main.py
Please cite our work if you feel the paper or the code are helpful.
@article{wang2023learning,
title={Learning to Generate Parameters of ConvNets for Unseen Image Data},
author={Wang, Shiye and Feng, Kaituo and Li, Changsheng and Yuan, Ye and Wang, Guoren},
journal={arXiv preprint arXiv:2310.11862},
year={2023}
}