Metadata-Version: 2.1
Name: deepclustering
Version: 0.0.3
Summary: UNKNOWN
Home-page: https://github.com/jizongFox/deep-clustering-toolbox
Author: Jizong Peng
Author-email: jizong.peng.1@etsmtl.net
License: MIT
Description: # deep-clustering-toolbox
        #### PyTorch Vision toolbox not only for deep-clustering
        ### Introduction
        I still use this repo for research propose. I update some modules frequently to make the framework flexible enough.
        
        This repo contains the base code for a deep learning framework using `PyTorch`, to benchmark algorithms for various dataset.
        The current version supports `MNIST`, `CIFAR10`, `SVHN` and `STL-10` for semisupervised and unsupervised learning.
        `ACDC`, `Promise12`, `WMH` and so on are supported as segmentation counterpart.
        
        #### Features:
        >- Powerful cmd parser using `yaml` module, providing flexible input formats without predefined argparser.
        >- Automatic checkpoint management adapting to various settings
        >- Automatic meter recording and experimental status plotting using matplotlib and threads
        >- Various build-in loss functions and help tricks and assert statements frequently used in PyTorch Framework, such as `disable_tracking_bn`, `ema`, `vat`, etc.
        >- Various post-processing tools such as Viewer for Medical image segmentations, multislice_viwers for 3D dataset real-time debug
        and report script for experimental summaries.
        >- Extendable modules for rapid development.
        
        #### Several projects are benefited from this scalable framework, builing top on this, including:
        
        + DeepClustering implemented for
        >- `Invariant Information Clustering for Unsupervised Image Classification and Segmentation`,
        >- `Learning Discrete Representations via Information Maximizing Self-Augmented Training`,
        >- [`Information based Deep Clustering: An experimental study`](https://github.com/jizongFox/DeepClusteringProject)
        + SemiSupervised classification for
        >- `Semi-Supervised Learning by Augmented Distribution Alignment`,
        >- `Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning`,
        >- `Temporal Ensembling for Semi-Supervised Learning`,
        >- `Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results`
        + SemiSupervised Segmentation for
        >- `Adversarial Learning for Semi-Supervised Semantic Segmentation`,
        >- `Semi-Supervised and Task-Driven Data Augmentation`,
        >- [`Deep Co-Training for Semi-Supervised Image Segmentation`](https://arxiv.org/abs/1903.11233)
        + Discretely-constrained CNN for
        >- [`Discretely-constrained deep network for weakly-supervised segmentation`](https://github.com/jizongFox/Discretly-constrained-CNN/),  
        >- `Mutual information based segmentation on medical imaging`
        
        
        They are examples how to develop research framework with the assistance of our proposed `deep-clustering-toolbox`.
        ___
        ### Playground
        
        Several papers have been implemented based on this framework. I store them in the `playground` folder. The papers include:
        
        >- [`Auto-Encoding Variational Bayes`](https://arxiv.org/abs/1312.6114)
        >- [`mixup: BEYOND EMPIRICAL RISK MINIMIZATION`](https://arxiv.org/pdf/1710.09412.pdf)
        >- [`MINE: Mutual Information Neural Estimation`](https://arxiv.org/abs/1801.04062)
        >- [`Averaging Weights Leads to Wider Optima and Better Generalization`](https://arxiv.org/pdf/1803.05407.pdf)
        >- [`THERE ARE MANY CONSISTENT EXPLANATIONS OF UNLABELED DATA: WHY YOU SHOULD AVERAGE`](https://arxiv.org/pdf/1806.05594.pdf)
        >- [`Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation`](https://arxiv.org/abs/1904.06346)
        
        
        ---
        ### Installation
        ```bash
        git clone https://github.com/jizongFox/deep-clustering-toolbox.git
        cd deep-clustering-toolbox  
        python setup install # for those who do not want to make changes immediately.
        # or
        python setup develop # for those who want to modify the code and make the impact immediate.
        
        ```
        Or very simply
        ```bash
        pip install deepclustering
        ```
        ### Citation
        If you feel useful for your project, please consider citing this work.
        ```latex
        @article{peng2019deep,
          title={Deep Co-Training for Semi-Supervised Image Segmentation},
          author={Peng, Jizong and Estradab, Guillermo and Pedersoli, Marco and Desrosiers, Christian},
          journal={arXiv preprint arXiv:1903.11233},
          year={2019}
        }
        ```
        
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