convcp

This is the sample code for hierarchical competitive learning which enables robust and effective unsupervised representation learning in conventional CNNs.

It has been presented in 1st Workshop on Shared Visual Representations in Human and Machine Intelligence (SVRHM), NeurIPS 2019. You can find the detailed explanation of the methods in our arXiv pre-print.

The code is implemented as an additional module of Chainer with GPU power, and involves samples of image discrimination tasks with MNIST, CIFAR-10, and ImageNet.

test environment

• CentOS 7.6
• Python 2.7

Requirements

• Chainer v.4.x
• Cupy v.4.x
• datasets
  • MNIST
  • CIFAR
  • ImageNet

setup virtualenv

cd
mkdir envs
virtualenv --system-site-packages envs/chainer
source envs/chainer/bin/activate
pip install -U setuptools pip
pip install 'cupy<5.0' 'chainer<5.0' 
pip install Pillow matplotlib tqdm h5py Cython

Download MNIST & CIFAR-10 datasets

python prep_data.py

MNIST

baseline by BP learning

python train_cifar.py -g 0 -i 3000 -c 10 -n \
    -D mnist.npz -M mnist -K fc.conv1

competitive learning

python train_cifar_cp.py -g 0 -i 600 -E 20 -C 1 \
    -D mnist.npz -M mnist_cp -K model.conv2 -O mnist_cp
python train_cifar.py -g 0 -i 3000 -c 10 -n \
    -D mnist.npz -M mnist_cp -W ../mnist_cp/model_fin.h5

CIFAR

baseline by BP learning

python train_cifar.py -g 0 -i 15000 -n \
    -M lenet -K fc.conv1

competitive learning

python train_cifar_cp.py -g 0 -E 200 \
    -M lenet_cp -K model.conv2 -O lenet_cp
python train_cifar.py -g 0 -i 15000 -n \
    -M lenet_cp -W ../lenet_cp/model_fin.h5

ImageNet

For the samples of ImageNet, you have to deploy the image data under 'convcp/ImageNet' directory. Please check 'all_train.lst' and 'all_val.lst' for the actual required directory structure for the dataset.

competitive learning

python train_alex_cp.py -g 0 -E 300 -C 10 -s 100 -B 8 -M alex_cp -O alex_cp

fine tuning

python train_alex.py -g 0 -i 20000 -n \
    -M alex_cp -W ../alex_cp/model_fin_cp.h5 -O alex_ft1 \
    --lr 0.01 
python train_alex.py -g 0 -i 20000 -n \
    -M alex_cp -W ../alex_cp/model_fin_cp.h5 -O alex_ft2 \
    --lr 0.001 -F alex_ft1/model_fin_bp_ft.h5 
python train_alex.py -g 0 -i 20000 -n \
    -M alex_cp -W ../alex_cp/model_fin_cp.h5 -O alex_ft3 \
    --lr 0.0001 -F alex_ft2/model_fin_bp_ft.h5 

test top-5 error

python test_alex_top5.py -g 0 \
-M alex_cp -W ../alex_cp/model_fin_cp.h5 \
    -F alex_ft3/model_fin_bp_ft.h5