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.
Deep Learning
Explore Deep Learning through related topics and the articles other pages reference most.
Explore articles
Browse subtopics: Interpretability
Articles that also belong to these categories. Counts cover all of Deep Learning.
All articles (334)Machine Learning (188)Neural Networks (81)Computer Vision (53)Training & Optimization (41)Natural Language Processing (37)Generative AI (31)Model Architecture (22)Transformer Models (17)Artificial Intelligence (16)Large Language Models (12)People (12)Reinforcement Learning (12)AI Inference (11)Developer Tools (11)Mathematics (11)Interpretability (8)AI Infrastructure (7)Speech & Audio AI (7)AI Hardware (6)AI History (5)AI Research (5)Google DeepMind (5)Open Source AI (5)AI Companies (4)AI Compute (4)AI for Science (4)Algorithms (4)Model Evaluation (4)Robotics (4)AI Models (3)Google (3)Meta AI (3)OpenAI (3)AI Alignment (2)AI Tools & Products (2)Chinese AI (2)Data & Datasets (2)Healthcare AI (2)MLOps (2)Microsoft (2)Multimodal AI (2)NVIDIA (2)Software Development (2)Statistics (2)AI Benchmarks (1)AI Ethics (1)AI Safety (1)Data Science (1)Diffusion Models (1)Embodied AI (1)Image Generation (1)Information Retrieval (1)Programming Languages (1)Prompt Engineering (1)Research Organizations (1)Video Generation (1)
Showing 1-8 of 8 articles
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