Chainer implementation of DeConvNet [1] and DeSaliNet [2]. These are the technics that visualize feature maps of each layers of convolutional networks. Currently, this implementation supports VGGNet and AlexNet.
| Input | conv5 | fc8 | |
|---|---|---|---|
| DeConvNet |  |
 |
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| DeSaliNet | |
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- Chainer
- CuPy (for GPU support)
- OpenCV
python run.py --input images/bird.png --arch alex --gpu 0
python run.py --input images/bird.png --arch vgg --gpu 0
python run.py --input images/bird.png --arch alex --mask --gpu 0
python run.py --input images/bird.png --arch vgg --mask --gpu 0
- [1] Matthew D. Zeiler and Rob Fergus, "Visualizing and Understanding Convolutional Networks", https://cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf
- [2] Aravindh Mahendran and Andrea Vedaldi, "Salient deconvolutional networks", https://www.robots.ox.ac.uk/~vedaldi/assets/pubs/mahendran16salient.pdf