This is an old version of some implementations, please visit https://github.com/pytorch-lumo/lumo for the best experiment management library and its derived projects.

Thexp-implement

Reimplement of some papers I interested. All implementations are based on my another pytorch framework thexp, and are as elegant and simple as I can.

How to run

git clone https://github.com/sailist/thexp-implement

You need to instal my another library thexp.

pip install thexp

When first run, you need to specify the dataset root: open data/dataxy.py, and change the value of the variable root. That's the only thing you need to do before running some scripts.

And, find the script in trainers/ and directly run it.

For example, if you want to reproduce the result of fixmatch, you can find it in trainers/semisupervised/fixmatch.py, and run it by using the code below:

python trainers/semisupervised/fixmatch.py

# or
cd trainers/semisupervised
python fixmatch.py

the working directory will be changed automaticaly, so you just need to run it.

Implementation list

Here list the paper reimplemented in this repo.

Supervised baseline

including WRN-28-2, WRN-28-10, and Resnet{20, 32, 44, 50, 56},

Only use basic cross-entropy loss to optimize the model, and the data use four common augmentation methods:weak, weak+mixup, strong, strong+mixup

weak means random horizontal flip and random crop, and strong means weak + RandAugment , please see the code for details.

python3 trainers/supervised/mixup.py
python3 trainers/supervised/strong_aug.py
python3 trainers/supervised/weak_aug.py
python3 trainers/supervised/strong_mixup.py

For most case, Strong or Strong+mixup will reach the best results.

[1] RandAugment: Practical automated data augmentation with a reduced search space, https://arxiv.org/pdf/1909.13719.pdf

[2] mixup: Beyond Empirical Risk Minimization, https://arxiv.org/abs/1710.09412

Semi-Supervised

see semisupervised/README.md for details

reimplement list:

• Interpolation Consistency Training for Semi-Supervised Learning
• MixMatch: A Holistic Approach to Semi-Supervised Learning
• FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Noisy Label

see noisylabel/README.md for details

reimplement list:

• MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks
• Probabilistic End-to-end Noise Correction for Learning with Noisy Labels(Pencil)
• Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels (MentorMix)
• Distilling Effective Supervision from Severe Label Noise (IEG)
• O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks

Meta-learning

see metalearning/README.md for details

reimplement list:

• Distilling Effective Supervision from Severe Label Noise (IEG)
• Learning to Reweight Examples for Robust Deep Learning (L2R)