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CoTeaching++

This repository improves CoTeaching Co-teaching Robust Training of Deep Neural Networks with Extremely Noisy Labels.

It uses the small-loss trick and samples which are predicted equivalently by the two networks.

It is implemented by TensorFlow

CoTeaching+

CoTeaching+ is the ICML'19 paper How does Disagreement Help Generalization against Label Corruption?

Difference between CoTeaching++ and CoTeaching+

CoTeaching++ is a little different from CoTeaching+

CoTeaching+ selects the samples which are predicted differently by the two networks.

Usage

You can install TensorFlow1.4 cuda8 cudnn6

To see the improvements between CoTeaching and CoTeaching++

Here are examples:

${dataset_name} can be cifar10 cifar100

$ python main_tf.py --dataset ${dataset_name} --noise_type symmetric --fr_type type_1 --batch_size 128 --noise_rate 0.2 --mode_type coteaching
$ python main_tf.py --dataset ${dataset_name} --noise_type symmetric --fr_type type_1 --batch_size 128 --noise_rate 0.2 --mode_type coteaching_plus

$ python main_tf.py --dataset ${dataset_name} --noise_type symmetric --fr_type type_1 --batch_size 128 --noise_rate 0.5 --mode_type coteaching
$ python main_tf.py --dataset ${dataset_name} --noise_type symmetric --fr_type type_1 --batch_size 128 --noise_rate 0.5 --mode_type coteaching_plus

$ python main_tf.py --dataset ${dataset_name} --noise_type pairflip --fr_type type_1 --batch_size 128 --noise_rate 0.45 --mode_type coteaching
$ python main_tf.py --dataset ${dataset_name} --noise_type pairflip --fr_type type_1 --batch_size 128 --noise_rate 0.45 --mode_type coteaching_plus

Notion

You can replace this line

NonEqual = tf.equal(pred1, pred2)

in model_tf.py by

NonEqual = tf.not_equal(pred1, pred2)

Then this repository is changed to implement CoTeaching+

Improments betwenn CoTeaching and CoTeaching++

CoTeaching        CoTeaching++
CoTeaching         CoTeaching++
CoTeaching         CoTeaching++
CoTeaching         CoTeaching++
CoTeaching         CoTeaching++
CoTeaching         CoTeaching++

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