Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al.  2017]  are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
<fluency> Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al.  2017]  are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al. , 2017] are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .