Unsupervised Learning on Neural Network Outputs
This repo contains the experiment code in paper
Unsupervised Learning on Neural Network Outputs: with Application in Zero-shot Learning
Yao Lu
International Joint Conference on Artificial Intelligence (IJCAI) 2016
The paper presents a new zero-shot learning method, which achieved the state-of-the-art results on ImageNet 2011fall (14.2 million images and 21841 classes).
The CNN model is GoogeLeNet with [Caffe] (http://caffe.berkeleyvision.org/) implementation. The image format convertor (image2hdf5) is from Toronto Deep Learning.
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 into HDF5 format with
- uncompress.sh
- correct_format.sh
- image2hdf5.sh
compute the CNN outputs of GoogLeNet of the images with
- caffe_outputs.py
compute PCA and ICA on the CNN outputs with
- cov.py
- whitening.py
- ica.py
compute the MDS features of WordNet graph with
- similarity_mat.py
- mds_distance_mat.m
run zero-shot learning experiments with
- 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!
