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/10.1145/3581783.3611794<i>dmrib-weights</i> is the file for pre-trained weights. <br><i>DMRIB-main </i>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 <code>DMRIB</code> train on the <code>EdgeMnist</code> dataset.Before the training step, you need to set the <code>CUDA_VISIBLE_DEVICES</code>, because of the <code>faiss</code> will use all gpu. It means that it will cause some error if you using <code>tensor.to()</code> to set a specific device.set environment.<pre><pre>export CUDA_VISIBLE_DEVICES=0<br></pre></pre>train the pretext model. First, we need to run the pretext training script <code>src/train_pretext.py</code>. We use simclr-style to training a self-supervised learning model to mine neighbors information. The pretext config commonly put at <code>configs/pretext</code>. You just need to run the following command in you terminal:<pre><pre>python train_pretext.py -f ./configs/pretext/pretext_EdgeMnist.yaml<br></pre></pre>train the self-label clustering model. Then, we could use the pretext model to training clustering model via <code>src/train_scan.py</code>.<pre><pre>python train_scan.py -f ./configs/scan/scan_EdgeMnist.yaml<br></pre></pre>After that, we use the fine-tune script to train clustering model <code>scr/train_selflabel.py</code>.<pre><pre>python train_selflabel.py -f ./configs/scan/selflabel_EdgeMnist.yaml<br></pre></pre>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 <code>src/train_dmrib.py</code>.<pre><pre>python train_dmrib.py -f ./configs/dmrib/dmrib_EdgeMnist.yaml<br></pre></pre>ValidationNote: you can find the pre-train weights in the file <code><em>dmrib-weights</em></code>. And put the pretrained models into the following folders <code>path to/{config.train.log_dir}/{results}/{config.dataset.name}/eid-{config.experiment_id}/dmrib/final_model.pth</code>, respectively. For example, if you try to validate the <code>EdgeMnist</code> dataset, the default folder is <code>./experiments/results/EdgeMnist/eid-0/dmrib</code>. And then, put the pretrained model <code>edge-mnist.pth</code> into this folder and rename it to <code>final_model.pth</code>.If you do not want to use the default setting, you have to modify the line 58 of the <code>validate.py</code>.<pre><pre>python validate.py -f ./configs/dmrib/dmrib_EdgeMnist.yaml<br></pre></pre>CreditThanks: <code>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.</code>Citation<pre><pre>Guanzhou Ke, Yang Yu, Guoqing Chao, Xiaoli Wang, Chenyang Xu,<br>and Shengfeng He. 2023. Disentangling Multi-view Representations Be-<br>yond Inductive Bias. In Proceedings of the 31st ACM International Conference<br>on Multimedia (MM ’23), October 29–November 3, 2023, Ottawa, ON, Canada.<br>ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3581783.3611794</pre></pre><br>