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SiMVC & CoMVC

This repository provides the implementations of SiMVC and CoMVC, presented in the paper:

"Reconsidering Representation Alignment for Multi-view Clustering" by Daniel J. Trosten, Sigurd Løkse, Robert Jenssen and Michael Kampffmeyer, in CVPR 2021.

BibTeX:

@inproceedings{trostenMVC,
  title        = {Reconsidering Representation Alignment for Multi-view Clustering},
  author       = {Daniel J. Trosten and Sigurd Løkse and Robert Jenssen and Michael Kampffmeyer},
  year         = 2021,
  booktitle    = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}
}

Installation

Requires Python >= 3.7 (tested on 3.8)

To install the required packages, run:

pip install -r requirements.txt

from the root directory of the repository. Anaconda (or similar) is recommended.

Datasets

Included dataset

The following datasets are included as files in this project:

  • voc (VOC)
  • rgbd (RGB-D)
  • blobs_overlap_5 (Toy dataset with 5 clusters)
  • blobs_overlap (Toy dataset with 3 clusters)

Generating datasets

To generate training-ready datasets, run:

python -m data.make_dataset <dataset_1> <dataset_2> <...>

This will export the training-ready datasets to data/processed/<datset_name>.npz.

Currently, the following datasets can be generated without downloading additional files:

  • mnist_mv (E-MNIST)
  • fmnist (E-FMNIST)

Datasets that require additional downloads

  • ccv (CCV): Download the files from here, and place them in data/raw/CCV.
  • coil (COIL-20). Download the processed files from here, and place them in data/raw/COIL.

After downloading and extracting the files, run

python -m data.make_dataset ccv coil

to generate training-ready versions of CCV and COIL-20.

Preparing a custom dataset for training

Create <custom_dataset_name>.npz in data/processed/ with the following keys:

n_views: The number of views, V
labels: One-dimensional array of labels. Shape (n,)
view_0: Data for first view. Shape (n, ...)
  .
  .
  .
view_V: Data for view V. Shape (n, ...)

Alternatively, call

data.make_dataset.export_dataset(
    "<custom_dataset_name>",    # Name of the dataset
    views,                      # List of view-arrays
    labels                      # Label array
)

This will automatically export the dataset to an .npz file at the correct location.

Then, in the Experiment-config, set

dataset_config=Dataset("<custom_dataset_name>")

Experiment configuration

Experiment configs are nested configuration objects, where the top-level config is an instance of config.defaults.Experiment.

The configuration object for the contrastive model on E-MNIST, for instance, looks like this:

from config.defaults import Experiment, CNN, DDC, Fusion, Loss, Dataset, CoMVC, Optimizer


mnist_contrast = Experiment(
    dataset_config=Dataset(name="mnist_mv"),
    model_config=CoMVC(
        backbone_configs=(
            CNN(input_size=(1, 28, 28)),
            CNN(input_size=(1, 28, 28)),
        ),
        fusion_config=Fusion(method="weighted_mean", n_views=2),
        projector_config=None,
        cm_config=DDC(n_clusters=10),
        loss_config=Loss(
            funcs="ddc_1|ddc_2|ddc_3|contrast",
            # Additional loss parameters go here
        ),
        optimizer_config=Optimizer(
            learning_rate=1e-3,
            # Additional optimizer parameters go here
        ) 
    ),
    n_epochs=100,
    n_runs=20,
)

Running an experiment

In the src directory, run:

python -m models.train -c <config_name>

where <config_name> is the name of an experiment config from one of the files in src/config/experiments/ or from 'src/config/eamc/experiments.py' (for EAMC experiments).

Overriding config parameters at the command-line

Parameters set in the config object can be overridden at the command line. For instance, if we want to change the learning rate for the E-MNIST experiment below from 0.001 to 0.0001, and the number of epochs from 100 to 200, we can run:

python -m models.train -c mnist_contrast \
                       --n_epochs 200 \
                       --model_config__optimizer_config__learning_rate 0.0001

Note the double underscores to traverse the hierarchy of the config-object.

Evaluating an experiment

Run the evaluation script:

python -m models.evaluate -c <config_name> \ # Name of the experiment config
                          -t <tag> \         # The unique 8-character ID assigned to the experiment when calling models.train
                          --plot             # Optional flag to plot the representations before and after fusion.

Ablation studies and noise experiment

To run one of these experiments, execute the corresponding script in the src/scripts directory.