SOTAVerified

Interpretability Techniques for Deep Learning

Papers

No papers found.

Benchmark Results

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