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

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/10.1145/3581783.3611794dmrib-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/DMRIBThe official repos for ""Disentangling Multi-view Representations Beyond Inductive Bias"" (DMRIB)Status: Accepted in ACM MM 2023.Training stepWe 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.set environment. export CUDA_VISIBLE_DEVICES=0 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 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 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 ValidationNote: 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 CreditThanks: 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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