This is a PyTorch implementation of the paper
Factorizing Knowledge in Neural Networks(ECCV 2022)
Xingyi Yang, Jingwen Ye, Xinchao Wang
In this paper, we explore a novel and ambitious knowledge-transfer task, termed Knowledge Factorization~(KF). The core idea of KF lies in the modularization
and assemblability of knowledge: given a pretrained network model as input, KF aims to decompose it into several factor networks, each of which handles only a dedicated task and maintains task-specific knowledge factorized from the source network.
This project is released under the Apache 2.0 license.
If you find this project useful in your research, please consider cite:
@Article{yang2022knowledgefactor,
author = {Xingyi Yang, Jingwen Ye, Xinchao Wang},
title = {Factorizing Knowledge in Neural Networks},
journal = {European Conference on Computer Vision},
year = {2022},
}