Awesome

MRDD

The official repos for "Rethinking Multi-view Representation Learning via Distilled Disentangling" (MRDD)

[ArXiv]

:fire:NOTE:fire: I am really interested in "how to predict the missing view/modality via disentangling representations.". Please contact me if you have any potential ideas or if you want to discuss this topic with me. Thanks. ( guanzhouk [at] gmail.com or guanzhouk [at] bjtu.edu.cn both could find me. ) If you are also BJTU's student, you could directly face-to-face with me.

Abstract

Multi-view representation learning aims to derive robust representations that are both view-consistent and view-specific from diverse data sources. This paper presents an in-depth analysis of existing approaches in this domain, highlighting a commonly overlooked aspect: the redundancy between view-consistent and view-specific representations. To this end, we propose an innovative framework for multi-view representation learning, which incorporates a technique we term `distilled disentangling'. Our method introduces the concept of \textit{masked cross-view prediction}, enabling the extraction of compact, high-quality view-consistent representations from various sources, without incurring extra computational overhead. Additionally, we develop a distilled disentangling module that efficiently filters out consistency-related information from multi-view representations, resulting in purer view-specific representations. This approach significantly reduces redundancy between view-consistent and view-specific representations, enhancing the overall efficiency of the learning process. Our empirical evaluations reveal that higher mask ratios substantially improve the quality of view-consistent representations. Moreover, we find that reducing the dimensionality of view-consistent representations relative to that of view-specific representations further refines the quality of the combined representations.

• Status: Accepted at CVPR 2024 (Poster).

Training step

We show that how MRDD train on the EdgeMnist dataset.

1. train the consistent encoder.

We provide two ways to train the consistent encoder. First one is using distributed training manner:

torchrun --nproc_per_node=[GPU NUMS] --master-port=[PORT] train_consistency.py  -f [config file]`

In this setting, you should enable the term use_ddp in config file's train domain. As the same time, you could fill in the devices domain to declare how many GPUs that you want to use in training.

We also provide the single gpu version:

python train_consistency.py  -f [config file]

Also, you should disable the term use_ddp.

2. train the disentangling module.

For DDP:

torchrun --nproc_per_node=[GPU NUMS] --master-port=[PORT] train_disentangle.py  -f [config file]`

For single:

python train_disentangle.py  -f [config file]

Validation

Note: you can find the pre-train weigths at here.

python eval.py -f ./configs/dataset name/eval.yaml

And fill in the path of pretrained model to model_path domain.

Citation

@inproceedings{ke2023disentangling,
  title={Disentangling multi-view representations beyond inductive bias},
  author={Ke, Guanzhou and Yu, Yang and Chao, Guoqing and Wang, Xiaoli and Xu, Chenyang and He, Shengfeng},
  booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
  pages={2582--2590},
  year={2023}
}

@article{ke2024rethinking,
  title={Rethinking Multi-view Representation Learning via Distilled Disentangling},
  author={Ke, Guanzhou and Wang, Bo and Wang, Xiaoli and He, Shengfeng},
  journal={arXiv preprint arXiv:2403.10897},
  year={2024}
}