Skip to content

Reproducing code for the paper "Learning Discrete Representations via Information Maximizing Self-Augmented Training"

Notifications You must be signed in to change notification settings

weihua916/imsat

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

9 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Information Maximizing Self Augmented Training (IMSAT)

This is a reproducing code for IMSAT [1]. IMSAT is a method for discrete representation learning using deep neural networks. It can be applied to clustering and hash learning to achieve the state-of-the-art results. This is the work performed while Weihua Hu was interning at Preferred Networks.

Requirements

You must have the following already installed on your system.

  • Python 2.7
  • Chainer 1.21.0, sklearn, munkres

Quick start

For reproducing the experiments on MNIST datasets in [1], run the following codes.

  • Clustering with MNIST: python imsat_cluster.py
  • Hash learning with MNIST: python imsat_hash.py

calculate_distance.py can be used to calculate the perturbation range for Virtual Adversarial Training [2]. For MNIST dataset, we have already calculated the range.

Datasets

All the datasets used in the paper can be downloaded here.

Reference

[1] Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto and Masashi Sugiyama. Learning Discrete Representations via Information Maximizing Self-Augmented Training. In ICML, 2017. Available at http://arxiv.org/abs/1702.08720

[2] Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, and Shin Ishii. Distributional smoothing with virtual adversarial training. In ICLR, 2016.

About

Reproducing code for the paper "Learning Discrete Representations via Information Maximizing Self-Augmented Training"

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages