cc20002002/image_classification

COMP5328 - Advanced Machine Learning

Assignment 2 - Learning with Noisy Data

Author : Yutong Cao, Chen Chen, Yixiong Fang

Lecturer: Tongliang Liu

Tutors : Nicholas James, Zhuozhuo Tu, Liu Liu

Objectives: This project implements three algorithms based on support vector machine against class-dependent classification noise.

The first algorithm extends an existing Expectation Maximisation method to class-dependent classification noise. The method was based on: Expectation Maximisation by Biggio et al. [2011]

The second algorithm reproduces existing state-of-the-art algorithms which are robust to label noise. The algorithm is based on: Importance Reweighing by Liu and Tao [2016]

We also heuristically propose a `quick and dirty' approach, which we called: 3. Heuristic Approach by Relabelling

This code will compare the performance of these three algorithms on two well-known dataset:

1. MINIST
2. CIFAR

Both datasets are injected with label noise.

Reuirements:

• sklearn == 0.20.0
• multiprocessing
• numpy
• matplotlib
• densratio

Running Environment Setup

1. Make sure to put the dataset file mnist_dataset.npz and cifar_dataset.npz into the folder with name input_data under the Code directory. The tree structure of our algorithm is:

project
│   README.md
│   biggio11.pdf
│   ...
└───Code
│   └───algorithm
│   │    │   main.py
│   │    │   util.py
│   │    │   estimate_rho_PCA.py
│   └───input_data
│        │   mnist_dataset.npz
         │   cifar_dataset.npz
└───assignment2
    │   ...
    │   ...

2. Run main.py in Code/algorithm with the choose dataset and algorithm.

    --dset DSET      Set the dataset to use, 1 = MINIST, 2 = CIFAR. Default is
                   CIFAR.
    --method METHOD  Set the algorithm to run, 1 = Expectation Maximisation, 2 =
                   Importance Reweighting, 3 = Heuristic Approach. Default is
                   'Importance Reweighting'.
   

   For example, to run Expectation Maximisation on MINIST dataset, please run:

   python main.py --dset=1 --method=1
   

   If you do not set the parameter, the default would be running Importance Reweighting on CIFAR.

3. To run algorithm estimating flip rates rho, please run in Code/algorithm:

   python estimate_rho_PCA.py
   

All results will be auto-saved to result/{generated-time-dataname}.