Generating class saliency maps using deep ConvNets
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I'm trying to generate some image-specific class saliency maps using a trained convolutional neural network as described in this paper. Basically it computes the derivative of the input image with respect to the output class score, then those gradients can be served as a saliency map.
The authors in this paper use softmax as output layer so each class corresponds to a single neuron in the output layer. However my model is used to do binary classification and my output layer is a single neuron followed by a sigmoid unit, does it make sense that I use this method to get the saliency maps for both classes? If so, should I just use back-propagation to calculate the derivative of the input image with respect to the output score for both 0 and 1 classes?
Thanks!
deep-learning
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$begingroup$
I'm trying to generate some image-specific class saliency maps using a trained convolutional neural network as described in this paper. Basically it computes the derivative of the input image with respect to the output class score, then those gradients can be served as a saliency map.
The authors in this paper use softmax as output layer so each class corresponds to a single neuron in the output layer. However my model is used to do binary classification and my output layer is a single neuron followed by a sigmoid unit, does it make sense that I use this method to get the saliency maps for both classes? If so, should I just use back-propagation to calculate the derivative of the input image with respect to the output score for both 0 and 1 classes?
Thanks!
deep-learning
New contributor
$endgroup$
add a comment |
$begingroup$
I'm trying to generate some image-specific class saliency maps using a trained convolutional neural network as described in this paper. Basically it computes the derivative of the input image with respect to the output class score, then those gradients can be served as a saliency map.
The authors in this paper use softmax as output layer so each class corresponds to a single neuron in the output layer. However my model is used to do binary classification and my output layer is a single neuron followed by a sigmoid unit, does it make sense that I use this method to get the saliency maps for both classes? If so, should I just use back-propagation to calculate the derivative of the input image with respect to the output score for both 0 and 1 classes?
Thanks!
deep-learning
New contributor
$endgroup$
I'm trying to generate some image-specific class saliency maps using a trained convolutional neural network as described in this paper. Basically it computes the derivative of the input image with respect to the output class score, then those gradients can be served as a saliency map.
The authors in this paper use softmax as output layer so each class corresponds to a single neuron in the output layer. However my model is used to do binary classification and my output layer is a single neuron followed by a sigmoid unit, does it make sense that I use this method to get the saliency maps for both classes? If so, should I just use back-propagation to calculate the derivative of the input image with respect to the output score for both 0 and 1 classes?
Thanks!
deep-learning
deep-learning
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DerickShiDerickShi
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