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Found 4 papers, 1 papers with code
DeepROCK: Error-controlled interaction detection in deep neural networks
The complexity of deep neural networks (DNNs) makes them powerful but also makes them challenging to interpret, hindering their applicability in error-intolerant domains.
Decoy-enhanced Saliency Maps
Saliency methods can make deep neural network predictions more interpretable by identifying a set of critical features in an input sample, such as pixels that contribute most strongly to a prediction made by an image classifier.
Robust saliency maps with distribution-preserving decoys
In this work, we propose a data-driven technique that uses the distribution-preserving decoys to infer robust saliency scores in conjunction with a pre-trained convolutional neural network classifier and any off-the-shelf saliency method.
DeepPINK: reproducible feature selection in deep neural networks
In this paper, we describe a method to increase the interpretability and reproducibility of DNNs by incorporating the idea of feature selection with controlled error rate.
