Awesome

Deep Critical Learning (i.e., Deep Robustness) In The Era of Big Data

Here are related papers on the fitting and generalization of deep learning:

• ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State
• Understanding deep learning requires rethinking generalization
• A Closer Look at Memorization in Deep Networks
• ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks
  • Blog link: https://xinshaoamoswang.github.io/blogs/2020-06-07-Progressive-self-label-correction/
• Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters
• Derivative Manipulation: Example Weighting via Emphasis Density Funtion in the context of DL
  • Novelty: moving from loss design to derivative design

Please kindly cite the following papers if you find this repo useful.

@article{wang2022proselflc,
  title={ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State},
  author={Wang, Xinshao and Hua, Yang and Kodirov, Elyor and Mukherjee, Sankha Subhra and Clifton, David A and Robertson, Neil M},
  journal={bioRxiv},
  year={2022}
}
@inproceddings{wang2021proselflc,
  title={ {ProSelfLC}: Progressive Self Label Correction
  for Training Robust Deep Neural Networks},
  author={Wang, Xinshao and Hua, Yang and Kodirov, Elyor and Clifton, David A and Robertson, Neil M},
  booktitle={CVPR},
  year={2021}
}
@phdthesis{wang2020example,
  title={Example weighting for deep representation learning},
  author={Wang, Xinshao},
  year={2020},
  school={Queen's University Belfast}
}
@article{wang2019derivative,
  title={Derivative Manipulation for General Example Weighting},
  author={Wang, Xinshao and Kodirov, Elyor and Hua, Yang and Robertson, Neil},
  journal={arXiv preprint arXiv:1905.11233},
  year={2019}
}
@article{wang2019imae,
  title={{IMAE} for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters},
  author={Wang, Xinshao and Hua, Yang and Kodirov, Elyor and Robertson, Neil M},
  journal={arXiv preprint arXiv:1903.12141},
  year={2019}
}

PyTorch Implementation for ProSelfLC, Derivative Manipulation, Improved MAE

• Easy to install
• Easy to use
• Easy to extend: new losses, new networks, new datasets and batch loaders
• Easy to run experiments and sink results
• Easy to put sinked results into your technical reports and academic papers.

Demos with extra configurations to get deterministic results, so that we can make fair comparisons

• Proselflc: Training the shufflenetv2 on cifar-100 with a symmetric noise rate of 40%

• Proselflc: Training the resnet18 on cifar-100 with a symmetric noise rate of 40%

• Label smoothing: Training the shufflenetv2 on cifar-100 with a symmetric noise rate of 40%

• Proselflc: Training the bert transformers on deeploc dataset with unknown labels

Install

Set the Pipenv From Scratch

• sudo apt update && sudo apt upgrade
• sudo apt install python3.8
• curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
• python3.8 get-pip.py
• vim ~/.bashrc -> add export PATH="/home/ubuntu/.local/bin:$PATH" -> source ~/.bashrc
• pip3 install pipenv

Build env for this repo using pipenv

• git clone this repo
• cd this repo
• pipenv install -e . --skip-lock

How to use

Run experiments

• cd this repo
• pipenv shell
• pre-commit install
• run experimenets:
  • CIFAR-100: CUDA_VISIBLE_DEVICES=0 CUBLAS_WORKSPACE_CONFIG=:4096:8 TOKENIZERS_PARALLELISM=false python -W ignore tests/convnets_cifar100/trainer_calibration_vision_cifar100_covnets_proselflc.py
  • Protein classification: CUDA_VISIBLE_DEVICES=0 CUBLAS_WORKSPACE_CONFIG=:4096:8 TOKENIZERS_PARALLELISM=false python -W ignore tests/protbertbfd_deeploc/MS-with-unknown/test_trainer_2MSwithunknown_proselflc.py

Visualize results

The results are well sinked and organised, e.g.,

• CIFAR-100: experiments_records/cifar100_symmetric_noise_rate_0.4/shufflenetv2
• Protein classification: experiments_records/deeploc_prottrans_symmetric_noise_rate_0.0/Rostlab_prot_bert_bfd_seq/MS-with-unknown

Examples of sinked experimental configs and resutls

• The results are well sinked and organised, e.g., experiments_records/cifar100_symmetric_noise_rate_0.4/shufflenetv2/502_proselflc_warm0_20220606-150113

• Accuracy curve: shufflenetv2

• Loss curve: shufflenetv2

• accuracy_loss_normalised_entropy_max_p_metadata.xlsx

• params.csv

How to extend this repo

• Add dataset and dataloader: see examples in src/proselflc/slices/datain
• Add losses: see examples in src/proselflc/slices/losses
• Add networks: see examples in src/proselflc/slices/networks
• Add optimisers: see examples in src/proselflc/optim
• Extend the slicegetter: src/proselflc/slicegetter
• Write run scripts: see examples in tests/

Supplementary material

Talks

• 23rd Dec 2022, Shanghai DianJi University
• 12th Aug 2022, Southern University of Science and Technology
• 17th May 2022, Loughborough University

Link to CVPR 2021 Slide, Poster

Link to reviewers' comments

Notes and disclaimer

• For any specific research discussion or potential future collaboration, please feel free to contact me. <br />
• This work is a personal research project and in progress using personal time.

LICENSE