Learning Deep Parsimonious Representations
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AlexNet.py
AlexNet_clustering.py
CIFAR_input.py release version Nov 27, 2016
CIFAR_models.py
CUB_input.py
CUB_test_list.txt
CUB_train_list.txt
README.md
eval_clustering.py
exp_config.py
kmeans_plus_plus.py
kmeans_update.py
mini_batch_iter.py
nn_cell_lib.py release version Nov 27, 2016
run_test_model.py release version Nov 27, 2016
run_train_model.py
run_zero_shot.py
zero_shot_split.npz

README.md

Learning Deep Parsimonious Representations

This is the code for our NIPS'16 paper:

Please cite the above paper if you use our code.

The code is released under the MIT license.

Data

The configuration of data is as below,

Training

Run python run_train_model.py <exp_id> to train a model.

Here exp_id should be one of the function names provided in exp_config.py. For example, setting exp_id to CIFAR10_sample_clustering, it will train a sample clustering model on CIFAR10 dataset.

Testing

Run python run_test_model.py <exp_id> to test a model.

You need to specify the test_model_name and test_folder in exp_config.py before run.

Zero-Shot

Run python run_zero_shot.py CUB_zero_shot to train and test a Struct SVM on top of the learned feature.

We provide the train/val/test split used in our experiment as the file zero_shot_split.npz.

You need to specify the test_model_name and test_folder in exp_config.py before run.

Notes

  • The experiment results may differ slightly from what we reported in the paper, as the cross validation is performed based on random split of the training data.