DeepLIFT
DeepLIFT (Deep Learning Important FeaTures) is a feature attribution method for deep neural networks introduced by Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje at Stanford University in 2017 .
Interpretability
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Grad-CAM
Grad-CAM (Gradient-weighted Class Activation Mapping) is a technique for producing visual explanations from convolutional neural network (CNN) models by using the gradients of a target class flowing into the…
Integrated Gradients
Integrated Gradients (IG) is a feature-attribution method for explainable AI that explains a neural network prediction by assigning each input feature an importance score
Latent Space
A latent space is the vector space a machine learning model maps its inputs into, where each input becomes a point (a latent vector or latent code) and the geometry of the space carries information the raw…
Layer-wise Relevance Propagation (LRP)
Layer-wise Relevance Propagation (LRP) is an explainable AI method that explains the prediction of a deep neural network by propagating the model's output backward through the network, layer by layer, and…
Saliency map
A saliency map is an explainable AI visualization that highlights which parts of an input, most often the individual pixels of an image, most influenced a deep learning model's prediction.
SmoothGrad
SmoothGrad is a saliency map technique that reduces visual noise in gradient-based explanations of neural network predictions by averaging gradients over many noisy copies of the input.
Sparse autoencoder
A sparse autoencoder (SAE) is a neural network that adds a sparsity penalty to an autoencoder's training loss so that only a small number of hidden units activate for any given input, producing a wide