Factorizing Knowledge in Neural Networks

This is a PyTorch implementation of the paper

Factorizing Knowledge in Neural Networks(ECCV 2022)

Supplementary Material

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.

Licence

This project is released under the Apache 2.0 license.

Citation

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},
    }