torchdistill 1.1.4
A Modular, Configuration-Driven Framework for Knowledge Distillation. Trained models, training logs and configurations are available for ensuring the reproducibility.
License: MIT
Requires Python: >=3.10

![torchdistill logo](https://raw.githubusercontent.com/yoshitomo-matsubara/torchdistill/main/docs/source/_static/images/logo-color.png)

# torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation
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[![GitHub Discussions](https://img.shields.io/github/discussions/yoshitomo-matsubara/torchdistill)](https://github.com/yoshitomo-matsubara/torchdistill/discussions)
[![DOI:10.1007/978-3-030-76423-4_3](https://zenodo.org/badge/DOI/10.1007/978-3-030-76423-4_3.svg)](https://doi.org/10.1007/978-3-030-76423-4_3)
[![DOI:10.18653/v1/2023.nlposs-1.18](https://zenodo.org/badge/DOI/10.18653/v1/2023.nlposs-1.18.svg)](https://doi.org/10.18653/v1/2023.nlposs-1.18)

***torchdistill*** (formerly *kdkit*) offers various state-of-the-art knowledge distillation methods 
and enables you to design (new) experiments simply by editing a declarative yaml config file instead of Python code. 
Even when you need to extract intermediate representations in teacher/student models, 
you will **NOT** need to reimplement the models, that often change the interface of the forward, but instead 
specify the module path(s) in the yaml file. Refer to [these papers](https://github.com/yoshitomo-matsubara/torchdistill#citation) for more details.  

In addition to knowledge distillation, this framework helps you design and perform general deep learning experiments
(**WITHOUT coding**) for reproducible deep learning studies. i.e., it enables you to train models without teachers 
simply by excluding teacher entries from a declarative yaml config file. 
You can find such examples below and in [configs/sample/](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/).   

In December 2023, ***torchdistill*** officially joined [PyTorch Ecosystem](https://pytorch.org/ecosystem/).

When you refer to ***torchdistill*** in your paper, please cite [these papers](https://github.com/yoshitomo-matsubara/torchdistill#citation) 
instead of this GitHub repository.  
**If you use** ***torchdistill*** **as part of your work, your citation is appreciated and motivates me to maintain and upgrade this framework!** 


## Documentation
You can find the API documentation and research projects that leverage ***torchdistill*** at https://yoshitomo-matsubara.net/torchdistill/


## Forward hook manager
Using **ForwardHookManager**, you can extract intermediate representations in model without modifying the interface of its forward function.  
[This example notebook](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/demo/extract_intermediate_representations.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/main/demo/extract_intermediate_representations.ipynb) [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/yoshitomo-matsubara/torchdistill/blob/main/demo/extract_intermediate_representations.ipynb) 
will give you a better idea of the usage such as knowledge distillation and analysis of intermediate representations.

E.g., extract intermediate representations (feature map) of ResNet-18 for a random input batch
```python
import torch
from torchvision import models
from torchdistill.core.forward_hook import ForwardHookManager

# Define a model and choose torch device
model = models.resnet18(pretrained=False)
device = torch.device('cpu')

# Register forward hooks for modules of your interest
forward_hook_manager = ForwardHookManager(device)
forward_hook_manager.add_hook(model, 'conv1', requires_input=True, requires_output=False)
forward_hook_manager.add_hook(model, 'layer1.0.bn2', requires_input=True, requires_output=True)
forward_hook_manager.add_hook(model, 'fc', requires_input=False, requires_output=True)

# Define a random input batch and run the model
x = torch.rand(32, 3, 224, 224)
y = model(x)

# Extract input and/or output of the modules
io_dict = forward_hook_manager.pop_io_dict()
conv1_input = io_dict['conv1']['input']
layer1_0_bn2_input = io_dict['layer1.0.bn2']['input']
layer1_0_bn2_output = io_dict['layer1.0.bn2']['output']
fc_output = io_dict['fc']['output']
```


## 1 experiment → 1 declarative PyYAML config file
In ***torchdistill***, many components and PyTorch modules are abstracted e.g., models, datasets, optimizers, losses, 
and more! You can define them in a declarative PyYAML config file so that can be seen as a summary of your experiment, 
and in many cases, you will **NOT need to write Python code at all**. 
Take a look at some configurations available in [configs/](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/). 
You'll see what modules are abstracted and how they are defined in a declarative PyYAML config file to design an experiment.  

E.g., instantiate CIFAR-10 datasets with a declarative PyYAML config file
```python
from torchdistill.common import yaml_util
config = yaml_util.load_yaml_file('./test.yaml')
train_dataset = config['datasets']['cifar10/train']
test_dataset = config['datasets']['cifar10/test']
```

**test.yaml**
```yaml
datasets:
  cifar10/train: !import_call
    key: 'torchvision.datasets.CIFAR10'
    init:
      kwargs:
        root: &root_dir '~/datasets/cifar10'
        train: True
        download: True
        transform: !import_call
          key: 'torchvision.transforms.Compose'
          init:
            kwargs:
              transforms:
                - !import_call
                  key: 'torchvision.transforms.RandomCrop'
                  init:
                    kwargs:
                      size: 32
                      padding: 4
                - !import_call
                  key: 'torchvision.transforms.RandomHorizontalFlip'
                  init:
                    kwargs:
                      p: 0.5
                - !import_call
                  key: 'torchvision.transforms.ToTensor'
                  init:
                - !import_call
                  key: 'torchvision.transforms.Normalize'
                  init:
                    kwargs: &normalize_kwargs
                      mean: [0.49139968, 0.48215841, 0.44653091]
                      std: [0.24703223, 0.24348513, 0.26158784]
  cifar10/test: !import_call
    key: 'torchvision.datasets.CIFAR10'
    init:
      kwargs:
        root: *root_dir
        train: False
        download: True
        transform: !import_call
          key: 'torchvision.transforms.Compose'
          init:
            kwargs:
              transforms:
                - !import_call
                  key: 'torchvision.transforms.ToTensor'
                  init:
                - !import_call
                  key: 'torchvision.transforms.Normalize'
                  init:
                    kwargs: *normalize_kwargs
```

If you want to use your own modules (models, loss functions, datasets, etc) with this framework, 
you can do so without editing code in the local package `torchdistill/`.  
See [the official documentation](https://yoshitomo-matsubara.net/torchdistill/usage.html) and [Discussions](https://github.com/yoshitomo-matsubara/torchdistill/discussions) for more details. 

## Benchmarks

[Top-1 validation accuracy for ILSVRC 2012 (ImageNet)](https://yoshitomo-matsubara.net/torchdistill/benchmarks.html#imagenet-ilsvrc-2012)


## Examples
Executable code can be found in [examples/](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/examples/) such as
- [Image classification](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/examples/torchvision/image_classification.py): ImageNet (ILSVRC 2012), CIFAR-10, CIFAR-100, etc
- [Object detection](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/examples/torchvision/object_detection.py): COCO 2017, etc
- [Semantic segmentation](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/examples/torchvision/semantic_segmentation.py): COCO 2017, PASCAL VOC, etc
- [Text classification](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/examples/hf_transformers/text_classification.py): GoEmotions, etc
- [GLUE](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/examples/hf_transformers/general_language_understanding.py): CoLA, SST-2, MRPC, STS-B, QQP, MNLI, QNLI, RTE, WNLI, AX

For CIFAR-10 and CIFAR-100, some models are reimplemented and available as pretrained models in ***torchdistill***. 
More details can be found [here](https://github.com/yoshitomo-matsubara/torchdistill/releases/tag/v0.1.1).  

Some Transformer models fine-tuned by ***torchdistill*** for GLUE tasks are available at [Hugging Face Model Hub](https://huggingface.co/yoshitomo-matsubara). 
Sample GLUE benchmark results and details can be found [here](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/examples/hf_transformers#sample-benchmark-results-and-fine-tuned-models).


## Google Colab Examples
The following examples are available in [demo/](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/demo/). 
Note that these examples are for Google Colab users and compatible with Amazon SageMaker Studio Lab. 
Usually, [examples/](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/examples/) would be a better reference 
if you have your own GPU(s).

### CIFAR-10 and CIFAR-100
- Training without teacher models [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/main/demo/cifar_training.ipynb) [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/yoshitomo-matsubara/torchdistill/blob/main/demo/cifar_training.ipynb)
- Knowledge distillation [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/main/demo/cifar_kd.ipynb) [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/yoshitomo-matsubara/torchdistill/blob/main/demo/cifar_kd.ipynb)

### GLUE
- Fine-tuning without teacher models [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/main/demo/glue_finetuning_and_submission.ipynb) [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/yoshitomo-matsubara/torchdistill/blob/main/demo/glue_finetuning_and_submission.ipynb)
- Knowledge distillation [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/main/demo/glue_kd_and_submission.ipynb) [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/yoshitomo-matsubara/torchdistill/blob/main/demo/glue_kd_and_submission.ipynb)

These examples write out test prediction files for you to see the test performance at [the GLUE leaderboard system](https://gluebenchmark.com/).


## PyTorch Hub
If you find models on [PyTorch Hub](https://pytorch.org/hub/) or GitHub repositories supporting PyTorch Hub,
you can import them as teacher/student models simply by editing a declarative yaml config file.  

e.g., If you use a pretrained ResNeSt-50 available in [huggingface/pytorch-image-models](https://github.com/huggingface/pytorch-image-models)
(aka *timm*) as a teacher model for ImageNet dataset, you can import the model via PyTorch Hub with the following entry 
in your declarative yaml config file.

```yaml
models:
  teacher_model:
    key: 'resnest50d'
    repo_or_dir: 'huggingface/pytorch-image-models'
    kwargs:
      num_classes: 1000
      pretrained: True
```


## How to setup
- Python >= 3.9
- pipenv (optional)

### Install by pip/pipenv
```
pip3 install torchdistill
# or use pipenv
pipenv install torchdistill
```

### Install from this repository (not recommended)
```
git clone https://github.com/yoshitomo-matsubara/torchdistill.git
cd torchdistill/
pip3 install -e .
# or use pipenv
pipenv install "-e ."
```


## Issues / Questions / Requests / Pull Requests
Feel free to create an issue if you find a bug.  
If you have either a question or feature request, start a new discussion [here](https://github.com/yoshitomo-matsubara/torchdistill/discussions).
Please search through [Issues](https://github.com/yoshitomo-matsubara/torchdistill/issues) and [Discussions](https://github.com/yoshitomo-matsubara/torchdistill/discussions) and make sure your issue/question/request has not been addressed yet.

Pull requests are welcome. 
Please start with an issue and discuss solutions with me rather than start with a pull request. 

## Citation
If you use ***torchdistill*** in your research, please cite the following papers:  
[[Paper](https://link.springer.com/chapter/10.1007/978-3-030-76423-4_3)] [[Preprint](https://arxiv.org/abs/2011.12913)]  
```bibtex
@inproceedings{matsubara2021torchdistill,
  title={{torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation}},
  author={Matsubara, Yoshitomo},
  booktitle={International Workshop on Reproducible Research in Pattern Recognition},
  pages={24--44},
  year={2021},
  organization={Springer}
}
```

[[Paper](https://aclanthology.org/2023.nlposs-1.18/)] [[OpenReview](https://openreview.net/forum?id=A5Axeeu1Bo)] [[Preprint](https://arxiv.org/abs/2310.17644)]  
```bibtex
@inproceedings{matsubara2023torchdistill,
  title={{torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP}},
  author={Matsubara, Yoshitomo},
  booktitle={Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)},
  publisher={Empirical Methods in Natural Language Processing},
  pages={153--164},
  year={2023}
}
```


## Acknowledgments

This project has been supported by Travis CI's OSS credits and [JetBrain's Free License Programs (Open Source)](https://www.jetbrains.com/community/opensource/?utm_campaign=opensource&utm_content=approved&utm_medium=email&utm_source=newsletter&utm_term=jblogo#support) 
since November 2021 and June 2022, respectively.  
![PyCharm logo](https://resources.jetbrains.com/storage/products/company/brand/logos/PyCharm.svg)


## References
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/examples/image_classification.py) [pytorch/vision/references/classification/](https://github.com/pytorch/vision/blob/main/references/classification/)
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/examples/object_detection.py) [pytorch/vision/references/detection/](https://github.com/pytorch/vision/tree/main/references/detection/)
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/examples/semantic_segmentation.py) [pytorch/vision/references/segmentation/](https://github.com/pytorch/vision/tree/main/references/segmentation/)
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/examples/hf_transformers/text_classification.py) [huggingface/transformers/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification)
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/kd/) Geoffrey Hinton, Oriol Vinyals, Jeff Dean. ["Distilling the Knowledge in a Neural Network"](https://arxiv.org/abs/1503.02531) @ Deep Learning and Representation Learning Workshop: NIPS 2014
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/fitnet/) Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, Yoshua Bengio. ["FitNets: Hints for Thin Deep Nets"](https://arxiv.org/abs/1412.6550) @ ICLR 2015
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/fsp/) Junho Yim, Donggyu Joo, Jihoon Bae, Junmo Kim. ["A Gift From Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning"](http://openaccess.thecvf.com/content_cvpr_2017/html/Yim_A_Gift_From_CVPR_2017_paper.html) @ CVPR 2017
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/at/) Sergey Zagoruyko, Nikos Komodakis. ["Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer"](https://openreview.net/forum?id=Sks9_ajex) @ ICLR 2017
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/pkt/) Nikolaos Passalis, Anastasios Tefas. ["Learning Deep Representations with Probabilistic Knowledge Transfer"](http://openaccess.thecvf.com/content_ECCV_2018/html/Nikolaos_Passalis_Learning_Deep_Representations_ECCV_2018_paper.html) @ ECCV 2018
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/ft/) Jangho Kim, Seonguk Park, Nojun Kwak. ["Paraphrasing Complex Network: Network Compression via Factor Transfer"](http://papers.neurips.cc/paper/7541-paraphrasing-complex-network-network-compression-via-factor-transfer) @ NeurIPS 2018
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/dab/) Byeongho Heo, Minsik Lee, Sangdoo Yun, Jin Young Choi. ["Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons"](https://aaai.org/ojs/index.php/AAAI/article/view/4264) @ AAAI 2019
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/coco2017/ktaad/) Tong He, Chunhua Shen, Zhi Tian, Dong Gong, Changming Sun, Youliang Yan. ["Knowledge Adaptation for Efficient Semantic Segmentation"](https://openaccess.thecvf.com/content_CVPR_2019/html/He_Knowledge_Adaptation_for_Efficient_Semantic_Segmentation_CVPR_2019_paper.html) @ CVPR 2019
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/rkd/) Wonpyo Park, Dongju Kim, Yan Lu, Minsu Cho. ["Relational Knowledge Distillation"](http://openaccess.thecvf.com/content_CVPR_2019/html/Park_Relational_Knowledge_Distillation_CVPR_2019_paper.html) @ CVPR 2019
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/vid/) Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D. Lawrence, Zhenwen Dai. ["Variational Information Distillation for Knowledge Transfer"](http://openaccess.thecvf.com/content_CVPR_2019/html/Ahn_Variational_Information_Distillation_for_Knowledge_Transfer_CVPR_2019_paper.html) @ CVPR 2019
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/hnd/) Yoshitomo Matsubara, Sabur Baidya, Davide Callegaro, Marco Levorato, Sameer Singh. ["Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems"](https://dl.acm.org/doi/10.1145/3349614.3356022) @ Workshop on Hot Topics in Video Analytics and Intelligent Edges: MobiCom 2019
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/cckd/) Baoyun Peng, Xiao Jin, Jiaheng Liu, Dongsheng Li, Yichao Wu, Yu Liu, Shunfeng Zhou, Zhaoning Zhang. ["Correlation Congruence for Knowledge Distillation"](http://openaccess.thecvf.com/content_ICCV_2019/html/Peng_Correlation_Congruence_for_Knowledge_Distillation_ICCV_2019_paper.html) @ ICCV 2019
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/spkd/) Frederick Tung, Greg Mori. ["Similarity-Preserving Knowledge Distillation"](http://openaccess.thecvf.com/content_ICCV_2019/html/Tung_Similarity-Preserving_Knowledge_Distillation_ICCV_2019_paper.html) @ ICCV 2019
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/crd/) Yonglong Tian, Dilip Krishnan, Phillip Isola. ["Contrastive Representation Distillation"](https://openreview.net/forum?id=SkgpBJrtvS) @ ICLR 2020
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/coco2017/ghnd/) Yoshitomo Matsubara, Marco Levorato. ["Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks"](https://arxiv.org/abs/2007.15818) @ ICPR 2020
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/tfkd/) Li Yuan, Francis E.H.Tay, Guilin Li, Tao Wang, Jiashi Feng. ["Revisiting Knowledge Distillation via Label Smoothing Regularization"](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yuan_Revisiting_Knowledge_Distillation_via_Label_Smoothing_Regularization_CVPR_2020_paper.pdf) @ CVPR 2020
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/sskd/) Guodong Xu, Ziwei Liu, Xiaoxiao Li, Chen Change Loy. ["Knowledge Distillation Meets Self-Supervision"](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/898_ECCV_2020_paper.php) @ ECCV 2020
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/pad/) Youcai Zhang, Zhonghao Lan, Yuchen Dai, Fangao Zeng, Yan Bai, Jie Chang, Yichen Wei. ["Prime-Aware Adaptive Distillation"](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3317_ECCV_2020_paper.php) @ ECCV 2020
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/tree/main/configs/sample/ilsvrc2012/kr/) Pengguang Chen, Shu Liu, Hengshuang Zhao, Jiaya Jia. ["Distilling Knowledge via Knowledge Review"](https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Distilling_Knowledge_via_Knowledge_Review_CVPR_2021_paper.html) @ CVPR 2021
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/ilsvrc2012/ickd/) Li Liu, Qingle Huang, Sihao Lin, Hongwei Xie, Bing Wang, Xiaojun Chang, Xiaodan Liang. ["Exploring Inter-Channel Correlation for Diversity-Preserved Knowledge Distillation"](https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Exploring_Inter-Channel_Correlation_for_Diversity-Preserved_Knowledge_Distillation_ICCV_2021_paper.html) @ ICCV 2021
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/ilsvrc2012/dist/) Tao Huang, Shan You, Fei Wang, Chen Qian, Chang Xu. ["Knowledge Distillation from A Stronger Teacher"](https://proceedings.neurips.cc/paper_files/paper/2022/hash/da669dfd3c36c93905a17ddba01eef06-Abstract-Conference.html) @ NeurIPS 2022
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/ilsvrc2012/srd/) Roy Miles, Krystian Mikolajczyk. ["Understanding the Role of the Projector in Knowledge Distillation"](https://ojs.aaai.org/index.php/AAAI/article/view/28219) @ AAAI 2024
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/ilsvrc2012/kd_w_ls/) Shangquan Sun, Wenqi Ren, Jingzhi Li, Rui Wang, Xiaochun Cao. ["Logit Standardization in Knowledge Distillation"](https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Logit_Standardization_in_Knowledge_Distillation_CVPR_2024_paper.html) @ CVPR 2024
- [:mag:](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/ilsvrc2012/dist_plus/) Tao Huang, Shan You, Fei Wang, Chen Qian, Chang Xu. ["DIST+: Knowledge Distillation From a Stronger Adaptive Teacher"](https://ieeexplore.ieee.org/document/10938241) @ TPAMI (2025)