End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization

This repository has the source code for the paper "End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization"(CVPR19).

Citing this work

@inproceedings{jeongCVPR19,
    title= {End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization},
    author={Jeong, Yeonwoo and Kim, Yoonsung and Song, Hyun Oh},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2019}
}

Installation

• Python3.5
• Deep learning frame work : Tensorflow1.4 gpu Check https://github.com/tensorflow/tensorflow/tree/r1.4
• Ortools(6.6.4656) Check https://developers.google.com/optimization/introduction/download

Prerequisites

1. Generate directory and change path(=ROOT) in configs/path.py

ROOT = '(user enter path)'

cd (ROOT)
mkdir exp_results
mkdir cifar_processed

2. Download and unzip dataset Cifar-100

cd (ROOT)
wget https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
tar zxvf cifar-100-python.tar.gz

Processing Data

cd process
python cifar_process.py

Training Procedure

• In this code, we provide experiment code on 'Cifar-100'(cifar_exps/)

1. Metric learning model(cifar_exps/metric/)
   • Run train_model in main.py to train the model for specific parameter.
   • Run integrate_results_and_preprocess in main.py to integrate results and preprocess before running 'ours'.
2. Ours proposed in the paper(cifar_exps/ours/)
   • Run train_model in main.py to train the model for specific parameter.
   • Run integrate_results in main.py to integrate results.

Evaluation

• Evaluation code is in utils/evaluation.py.
• The performance of hash table structured contructed by ours method is evaluated with 3 different metric(NMI, precision@k, SUF).

License

MIT License