This code is for the paper "Confident Multiple Choice Learning".

It is tested under Ubuntu Linux 16.04.1 and Python 2.7 environment, and requries following Python packages to be installed:

• TensorFlow: version 1.0.0 or above. Only GPU version is available.
• Torchfile: version 0.0.2 or above.

Simple torchfile installation:

pip install torchfile

We provide the following datasets in torch format:

• CIFAR-10 whitened: pre-processed data (1.37GB)
• SVHN (excluding the extra dataset): pre-processed data (2.27GB)

• run_CMCL.sh: train the models using "Confident multiple choice learning".
• run_MCL.sh: train the models using "Multiple choice learning".
• run_IE.sh: train the models using "Independent ensemble".

python src/ensemble.py \
--dataset=cifar \
--model_type=resnet \
--batch_size=128 \
--num_model=5 \
--loss_type=cmcl_v0 \
--k=4 \
--beta=0.75 \
--feature_sharing=True \
--test=False

• dataset : supports cifar and svhn.
• model_type : supports vggnet, googlenet, and resnet.
• batch_size : we use batch size 128.
• num_model : number of models to ensemble.
• loss_type : supports independent, mcl, cmcl_v0, and cmcl_v1.
• k : overlap parameter.
• beta : penalty parameter.
• feature_sharing : use feature sharing if True.
• test : if True, test the result of previous training, otherwise run a new training.