https://github.com/bbenligiray/rml-cnn
Semi-Supervised Robust Deep Neural Networks for Multi-Label Classification
https://github.com/bbenligiray/rml-cnn
loss-functions multi-label-classification robust-machine-learning semi-supervised-learning
Last synced: 12 months ago
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Semi-Supervised Robust Deep Neural Networks for Multi-Label Classification
- Host: GitHub
- URL: https://github.com/bbenligiray/rml-cnn
- Owner: bbenligiray
- Created: 2018-09-04T08:23:58.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2019-12-30T12:24:56.000Z (over 6 years ago)
- Last Synced: 2025-05-06T06:46:46.966Z (about 1 year ago)
- Topics: loss-functions, multi-label-classification, robust-machine-learning, semi-supervised-learning
- Language: Python
- Homepage:
- Size: 134 KB
- Stars: 17
- Watchers: 2
- Forks: 2
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# RML-CNN
Code covering some of the experiments in the following paper:
[Cevikalp, H., Benligiray, B., Gerek, O. N.. (2019). Semi-Supervised Robust Deep Neural Networks for Multi-Label Image Classification. In Pattern Recognition.](https://www.sciencedirect.com/science/article/abs/pii/S0031320319304649)
Use [this](https://github.com/bbenligiray/nus_wide_formatter_SRN) to create a `nus_wide.h5` and [this](https://github.com/bbenligiray/ms_coco_formatter_SRN) to create a `ms_coco.h5` file. Download `resnet101_weights_tf.h5` from [here](https://gist.github.com/flyyufelix/65018873f8cb2bbe95f429c474aa1294). Put these in `/rml-cnn`, then run `run.sh` to run the experiments.
Alternatively, you can use the loss functions in `ml_loss` with any model/dataset you like, see `main.py` for reference. Note that your final layer's activation should be `None` for all loss functions (including softmax).