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PyTorch implementation for COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (CVPR 2021)

License: MIT License

Python 100.00%
clustering multi-view

2021-cvpr-completer's Issues

最大化互信息和对比学习的相关实验

大佬您好,有证明说 对比学习其实也是在做最大化互信息这件事情。但是我把模型的最大化互信息替换成infoNCE后(也就是您们组里面的CC那个实例级损失函数),发现效果没有那么惊艳,请问大佬尝试过相关实验吗?比如我在NoisyMnist数据集上尝试,发现infoNCE+重构的效果并不是很好,大概acc在76左右,而互信息+重构能够到达90+;我能想到的一些方面: 调参、归一化等问题。

关于NoisyMNIST数据集的第一个view

查看未处理的数据集时第一个view的值全为0,请问是否是正常状态?如果不是正常状态,可否重新上传正常的该数据集?谢谢!

关于损失函数

您好,
我尝试用您的模型跑自己的数据集(两个视图),但是出现了对比损失为负数,且聚类指标越来越低的情况。这可能是什么原因呢?

Require supplementary material

Dear author:
Hello guys, great work! Could you kindly post your supplementary material. I can't find it in Prof Peng Xi's website. I want to see the detail presentation of Eq 6.

With great thanks!

Why is Gaussian distribution?

Dear author, I don't quite understand one point. Is this representation Z discrete or continuous? If Z is discrete, why is the variational distribution Q simulated by Gaussian distribution instead of Bernoulli distribution?

about baseline AE2-Nets

AE2-Nets [1] is used as a baseline in the paper. But it seems that it's not originally designed for the incomplete scenario. Why can it be used as a baseline ? How is it adapted for such scenario ?


  1. Zhang C, Liu Y, Fu H. Ae2-nets: Autoencoder in autoencoder networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 2577-2585.

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