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Deep multimodal subspace clustering networks

Overview

This repository contains the implementation of the paper "Deep multimodal subspace clustering networks" by Mahdi Abavisani and Vishal M. Patel. The paper was posted on arXiv in May 2018.

"Deep multimodal subspace clustering networks" (DMSC) investigated various fusion methods for the task of multimodal subspace clustering, and suggested a new fusion technique called "affinity fusion" as the idea of integrating complementary information from two modalities with respect to the similarities between datapoints across different modalities.

Citation

Please use the following to refer to this work in publications:

Setup:

Dependencies:

Tensorflow, numpy, sklearn, munkres, scipy.

Data preprocessing:

Resize the input images of all the modalities to 32 × 32, and rescale them to have pixel values between 0 and 255. This is for keeping the hyperparameter selections suggested in Deep subspace clustering networks valid.

Save the data in a .mat file that includes verctorized modalities as separate matrices with the names modality_0,modality_1, ... ; labels in a vector with the name Labels; and number of modalities in the variable num_modalities.

A sample preprocessed dataset is available in: Data/EYB_fc.mat

Running the code

Affinity fusion :

Run affinity_fusion.py to do mutlimodal subspace clustering. For demo a pretrained model trained on EYB_fc is avilable in models/EYBfc_af.ckpt

Run the demo as:

python affinity_fusion.py --mat EYB_fc --model EYBfc_af

Pretraining:

Run pretrain_affinity_fusion.py to pretrain your networks.

For example:

python pretrain_affinity_fusion.py --mat EYB_fc --model mymodel --epoch 100000