#Unsupervised Learning on Neural Network Outputs This repo contains the experiment code in paper

Unsupervised Learning on Neural Network Outputs

The paper presents a new zero-shot learning method, which achieves the state-of-the-art results in ImageNet fall2011.

Instructions

download the following files from http://image-net.org/

• ILSVRC2012_img_train.tar (138G)
• ILSVRC2012_img_val.tar (6.3G)
• fall11_whole.tar (1.2T)

prepare the images intro HDF5 files, use

• uncompress.sh
• correct_format.sh
• image2hdf5.sh

compute the CNN outputs of GoogLeNet of the images, use

• caffe_outputs.py

compute PCA and ICA on the CNN outputs, use

• cov.py
• whitening.py
• ica.py

compute the MDS features of WordNet graph, use

• similarity_mat.py
• mds_distance_mat.m

run zero-shot learning experiments, use

• imagenet_1k_21k_idx.py
• imagenet_zero_shot_unseen_wnids.py
• make_zero_shot_mat.m
• zero_shot_random.py
• zero_shot_pca.py
• zero_shot_ica.py

Questions

If you have any question regarding the code and the experiments, please contact me ([email protected]). I would like to hear from you!