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Neural Manifold Clustering and Embedding (NMCE)

Code for paper Neural Manifold Clustering and Embedding (NMCE)

drawing

NMCE uses MCR2 objective to perform unsupervised clustering and embedding learning on non-linear manifolds. Data-augmentation is used to enforce constraint that makes the problem tractable. In the example above, the locality constraint is enforced with Gaussian noise augmentation, for image clustering, standard self-supervise learning augmentation is used.

Requirements

scipy
numpy
torch
sklearn
matplotlib
torchvision
tqdm

Experiments

The double spiral toy example is included in NMCE_toy.

COIL20 and COIL100:

First, decompresse the original datasets and run the following command to convert them:

python NMCE/convert_imgs.py --COIL_20_path /path_to_coil-20 --COIL_100_path /path_to_coil-100

Train COIL-20:

python NMCE/train_COIL20.py

Train COIL-100 (will take a while):

python NMCE/train_COIL100.py

CIFAR-10 with ResNet-18:

To select which GPU(s) to use, pass list of gpus to --gpu_ids argument. Specify path to dataset with --data_dir argument

Stage 1: Self-supervised learning with TCR (Total Coding Rate) objective:

python NMCE/train_selfsup.py --arch resnet18cifar --data cifar10 --data_dir ../../data/ --aug_name cifar_simclr_norm --loss totalcodingrate --z_dim 128 --epo 600 --bs 1024 --lr 0.3 --wd 1e-4 --eps 0.2 --z_weight 30. --gpu_ids [0] --fp16 --doc tcr_zw30

Evaluate network trained with TCR:

python NMCE/evaluate.py --arch resnet18cifar --data cifar10 --aug_name cifar_simclr_norm --feature_type proj --z_dim 128 --load_ep 600  --gpu_ids [0] --aug_avg 16 --doc tcr_zw30 --svm --knn --nearsub

Stage 2: Clustering with backbone frozen:

python NMCE/train_clustering.py --arch resnet18cifar --data cifar10 --data_dir ../../data/ --aug_name cifar_simclr_norm --z_dim 128 --epo 100 --bs 1024 --lr 0.3 --eps 0.2 --z_weight 0. --wd1 0.005 --wd2 0.005 --gpu_ids [0] --doc tcr_zw30 --load_ep 600 --seed 42

Stage 3: Fine-tune backbone with full NMCE objective:

python NMCE/train_clustering.py --arch resnet18cifar --data cifar10 --data_dir ../../data/ --aug_name cifar_simclr_norm --z_dim 128 --epo 100 --bs 1024 --lr 0.003 --eps 0.2 --z_weight 0. --wd1 0.0001 --wd2 0.0001 --gpu_ids [0] --doc tcr_zw30 --load_ep 700 --train_backbone

Cluster accuracy should be ~83%.

Acknowledgment:

This repo borrowed significantly from MCR2 and solo-learn repo.