SOTAVerified

Interpretability Techniques for Deep Learning

Papers

Showing 21–25 of 25 papers

TitleStatusHype
DeepNNK: Explaining deep models and their generalization using polytope interpolationCode0
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency MapsCode0
What Do Compressed Deep Neural Networks Forget?Code0
Contextual Explanation NetworksCode0
A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life PredictionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DASLog odds-ratio (pythia-6.9b)9.95—Unverified
2Linear probeLog odds-ratio (pythia-6.9b)3.42—Unverified
3Difference-in-meansLog odds-ratio (pythia-6.9b)2.91—Unverified
4k-meansLog odds-ratio (pythia-6.9b)1.87—Unverified
5PCALog odds-ratio (pythia-6.9b)1.81—Unverified
6LDALog odds-ratio (pythia-6.9b)0.27—Unverified
7RandomLog odds-ratio (pythia-6.9b)0.01—Unverified
#ModelMetricClaimedVerifiedStatus
1RISEInsertion AUC score0.57—Unverified
2HSIC-AttributionInsertion AUC score0.57—Unverified
3Kernel SHAPInsertion AUC score0.52—Unverified
4LIMEInsertion AUC score0.52—Unverified
5SaliencyInsertion AUC score0.46—Unverified
6Grad-CAMInsertion AUC score0.37—Unverified
7Integrated GradientsInsertion AUC score0.36—Unverified