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Data and codes for "Disentangling Multi-view Representations Beyond Inductive Bias"

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posted on 2023-10-06, 08:55 authored by Guanzhou KE, Yang YU, Guoqing CHAO, Xiaoli WANG, Chenyang XU, Shengfeng HeShengfeng He

This record contains the data and codes for this paper:

Guanzhou Ke, Yang Yu, Guoqing Chao, Xiaoli Wang, Chenyang Xu, and Shengfeng He. 2023. "Disentangling Multi-view Representations Beyond Inductive Bias." In Proceedings of the 31st ACM International Conference on Multimedia (MM '23), October 29–November 3, 2023, Ottawa, ON, Canada. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3581783.3611794

dmrib-weights is the file for pre-trained weights.
DMRIB-main is a copy of the project's GitHub Repository at https://github.com/Guanzhou-Ke/DMRIB

The official repos for ""Disentangling Multi-view Representations Beyond Inductive Bias"" (DMRIB)

  • Status: Accepted in ACM MM 2023.

Training step

We show that how DMRIB train on the EdgeMnist dataset.

Before the training step, you need to set the CUDA_VISIBLE_DEVICES, because of the faiss will use all gpu. It means that it will cause some error if you using tensor.to() to set a specific device.

  1. set environment.
export CUDA_VISIBLE_DEVICES=0
  1. train the pretext model. First, we need to run the pretext training script src/train_pretext.py. We use simclr-style to training a self-supervised learning model to mine neighbors information. The pretext config commonly put at configs/pretext. You just need to run the following command in you terminal:
python train_pretext.py -f ./configs/pretext/pretext_EdgeMnist.yaml
  1. train the self-label clustering model. Then, we could use the pretext model to training clustering model via src/train_scan.py.
python train_scan.py -f ./configs/scan/scan_EdgeMnist.yaml

After that, we use the fine-tune script to train clustering model scr/train_selflabel.py.

python train_selflabel.py -f ./configs/scan/selflabel_EdgeMnist.yaml
  1. training the view-specific encoder and disentangled. Finally, we could set the self-label clustering model as the consisten encoder. And train the second stage via src/train_dmrib.py.
python train_dmrib.py -f ./configs/dmrib/dmrib_EdgeMnist.yaml

Validation

Note: you can find the pre-train weights in the file dmrib-weights. And put the pretrained models into the following folders path to/{config.train.log_dir}/{results}/{config.dataset.name}/eid-{config.experiment_id}/dmrib/final_model.pth, respectively. For example, if you try to validate the EdgeMnist dataset, the default folder is ./experiments/results/EdgeMnist/eid-0/dmrib. And then, put the pretrained model edge-mnist.pth into this folder and rename it to final_model.pth.

If you do not want to use the default setting, you have to modify the line 58 of the validate.py.

python validate.py -f ./configs/dmrib/dmrib_EdgeMnist.yaml

Credit

Thanks: Van Gansbeke, Wouter, et al. "Scan: Learning to classify images without labels." Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part X. Cham: Springer International Publishing, 2020.

Citation

Guanzhou Ke, Yang Yu, Guoqing Chao, Xiaoli Wang, Chenyang Xu,
and Shengfeng He. 2023. Disentangling Multi-view Representations Be-
yond Inductive Bias. In Proceedings of the 31st ACM International Conference
on Multimedia (MM ’23), October 29–November 3, 2023, Ottawa, ON, Canada.
ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3581783.3611794


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