Partially View-aligned Representation Learning with Noise-robust Contrastive Loss
2021-CVPR-MvCLN
This repo contains the code and data of the following paper accepted by CVPR 2021
Requirements
pytorch==1.5.0
numpy>=1.18.2
scikit-learn>=0.22.2
munkres>=1.1.2
logging>=0.5.1.2
Configuration
The hyper-parameters, the training options (including the ratiao of positive to negative, aligned proportions and switch time) are defined in the args. part in run.py.
Datasets
The Scene-15 and Reuters-dim10 datasets are placed in “datasets” folder. The NoisyMNIST and Caltech101 datasets could be downloaded from Google cloud or Baidu cloud with password “rqv4”.
Usage
After setting the configuration and downloading datasets from the cloud desk, one could run the following code to verify our method on NoisyMNIST-30000 dataset for clustering task.
python run.py --data 3
The expected outputs are as follows:
******** Training begin, use RobustLoss: 1.0*m, use gpu