associative_deep_clustering

KO | EN

gitlite — search

#python #hacktoberfest #react #ai #typescript #machine-learning #deep-learning #linux #awesome #cli #vue #security #pytorch #openai

associative_deep_clustering

(★ 12)

No description available.

associative_deep_clustering 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

This repository contains code for the paper [Learning by Association - A versatile semi-supervised training method for neural networks (CVPR 2017)](https://vision.in.tum.de/_media/spezial/bib/haeusser_cvpr_17.pdf) 
and the follow-up works [Associative Domain Adaptation (ICCV 2017)](https://vision.in.tum.de/_media/spezial/bib/haeusser_iccv_17.pdf) and [Associative Deep Clustering - Training a classification network with no labels (GCPR 2018)]

It is implemented with TensorFlow. Please refer to the [TensorFlow documentation](https://www.tensorflow.org/install/) for further information.

The core functions are implemented in `semisup/backend.py`.
The files `train.py` and `eval.py` demonstrate how to use them. A quick example is contained in `mnist_train_eval.py`.

To run unsupervised (clustering) mode, use the `train_unsup2.py` script. For reference see also our paper. 
An example command with hyperparameters will be added soon.

In order to reproduce the results from the paper, please use the architectures and pipelines from the `{stl10,svhn,synth}_tools.py`. They are loaded automatically by setting the flag `package` in `{train,eval}.py` accordingly.

Before you get started, please make sure to add the following to your `~/.bashrc`:
```
export PYTHONPATH=/path/to/learning_by_association:$PYTHONPATH
```

Copy the file `semisup/tools/data_dirs.py.template` to `semisup/tools/data_dirs.py`, adapt the paths and .gitignore this file.

If you use the code, please cite the paper "Learning by Association - A versatile semi-supervised training method for neural networks" or "Associative Domain Adaptation":
```
@string{cvpr="IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"}
@InProceedings{haeusser-cvpr-17,
  author = 	 "P. Haeusser and A. Mordvintsev and D. Cremers",
  title = 	 "Learning by Association - A versatile semi-supervised training method for neural networks",
  booktitle = cvpr,
  year = 	 "2017",
}

@string{iccv="IEEE International Conference on Computer Vision (ICCV)"}
@InProceedings{haeusser-iccv-17,
  author = 	 "P. Haeusser and T. Frerix and A. Mordvintsev and D. Cremers",
  title = 	 "Associative Domain Adaptation",
  booktitle = iccv,
  year = 	 "2017",
}
```

For questions please contact Philip Haeusser (haeusser@cs.tum.edu).

associative_deep_clustering

associative_deep_clustering 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

associative_deep_clustering

associative_deep_clustering 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README