Awesome

Co-teaching - Robust training of deep neural networks with extremely noisy labels

Unofficial tensorflow implementation of the CIFAR-10 learned by Co-teaching. Official torch implementation is here

Publication </br> Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., and Sugiyama, M. Co-teaching: Robust training of deep neural networks with extremely noisy labels," Advances in Neural Information Processing Systems (NIPS), pp. 8536–8546, 2018.

1. Summary

For robust training on noisy labels, Co-teaching uses two neural networks. Each network selects its small-loss samples as clean samples, and feeds such clean samples to its peer network for futher training. Below figure demonstrates the overall procedures of Co-teaching. For each iteration, two networks forward-propagate the same mini-batch to identify clean samples. Then, each selected clean subset is back-propagated into peer network to update the model parameter.

2. Noise Injection and Network Architecture

• To inject noisy labels on CIFAR-10 dataset, the true label i was flipped to the randomly chosen label j with a probability tau. That is, tau is a given noise rate that determines the degree of noiseness on dataset.
• A densely connected neural networks (L=40, k=12)(Huang et al./ 2017) was used to train the noisy CIFAR-10.
• For the performance comparison, we compared the test loss of Co-teaching with that of Default. Defualt trained the noisy CIFAR-10 without any processing for noisy labels.

3. Environment

• Python 3.6.4
• Tensorflow-gpu 1.8.0 (pip install tensorflow-gpu==1.8.0)
• Tensorpack (pip install tensorpack)

4. How to Run

• Algorithm parameters

   -gpu_id: gpu number which you want to use.
   -method_name: method in {Default, Co-teaching}.
   -noise_rate: the rate which you want to corrupt.
   -log_dir: log directory to save the training/test error.
  

• Algorithm configuration

  Data augmentation and distortion are not applied, and training paramters are set to:

  Training epochs: 200
  Batch size: 128
  Learning rate: 0.1 (divided 5 at the approximately 50% and approximately 75% of the total number of epochs)
  Dataset: CIFAR-10
  

  These configurations can be easily modified at main.py:

  # gradient optimizer type
  optimizer = 'momentum'
  
  # total number of training epcohs
  total_epochs = 200
  
  # batch size
  batch_size = 128
  
  # learning rates used for training, and the time to use each learning rate.
  # e.g., lr=0.1 is used before 20,000 iterations, lr=0.02 is used before 30,000 iterations, lr=0.04 is used after 30,000 iterations
  lr_values = [0.1, 0.02, 0.004]
  lr_boundaries = [40000, 60000]
  
  # training algorithms
  if method_name == "Default":
      default(gpu_id, input_reader, total_epochs, batch_size, lr_boundaries, lr_values, optimizer, noise_rate, log_dir=log_dir)
  elif method_name == "Coteaching":
      coteaching(gpu_id, input_reader, total_epochs, batch_size, lr_boundaries, lr_values, optimizer, noise_rate, log_dir=log_dir)
  

• Running commend

   python main.py gpu_id method_name noise_rate log_dir
  

  This commend includes: (i) CIFAR-10 automatical download, (ii) noise injection, and (iii) neural network training.

5. Tutorial 1: Comparison of learning curves at the noise rate of 40%.

• We set tau to 0.4
• Running script

  #!/bin/sh
  main.py 0 Default 0.4 tutorial_1/Defulat
  main.py 0 Co-teaching 0.4 tutorial_1/Co-teaching
  

• Running result

6. Tutorial 2: Comparison of the best test error with varying noise rates.

• We used tau in {0.0, 0.1, 0.2, 0.3, 0.4} //from light noise to heavy noise
• Running script

  #!/bin/sh
  for i in 0.0 0.1 0.2 0.3 0.4
  do
    main.py 0 Default $i tutorial_2/Defulat/$i
    main.py 0 Co-teaching $i tutorial_2/Co-teaching/$i
  done
  

• Running result