Interpretability Techniques for Deep Learning
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Benchmarks
Most implemented
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Axiomatic Attribution for Deep Networks
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
A Unified Approach to Interpreting Model Predictions
RISE: Randomized Input Sampling for Explanation of Black-box Models
Papers
Time series saliency maps: explaining models across multiple domains
Traditional saliency map methods, popularized in computer vision, highlight individual points (pixels) of the input that contribute the most to the model's output. However, in time-series they offer limited insights as s…
Explainable Artificial Intelligence (XAI)Interpretability Techniques for Deep LearningPhotoplethysmography (PPG) heart rate estimationSeizure Detection+2Dissecting and Mitigating Diffusion Bias via Mechanistic Interpretability
Diffusion models have demonstrated impressive capabilities in synthesizing diverse content. However, despite their high-quality outputs, these models often perpetuate social biases, including those related to gender and …
Age/UnbiasedDecision MakingImage GenerationInterpretability Techniques for Deep Learning+1IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in Histopathology
Histopathological image analysis is crucial for accurate cancer diagnosis and treatment planning. While deep learning models, especially convolutional neural networks, have advanced this field, their "black-box" nature r…
ClassificationDenoisingExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)+1Explainable Deep Learning: A Visual Analytics Approach with Transition Matrices
The non-transparency of artificial intelligence (AI) systems, particularly in deep learning (DL), poses significant challenges to their comprehensibility and trustworthiness. This study aims to enhance the explainability…
Deep LearningExplainable Artificial Intelligence (XAI)Image GenerationInterpretability Techniques for Deep Learning+3CausalGym: Benchmarking causal interpretability methods on linguistic tasks
Language models (LMs) have proven to be powerful tools for psycholinguistic research, but most prior work has focused on purely behavioural measures (e.g., surprisal comparisons). At the same time, research in model inte…
BenchmarkingInterpretability Techniques for Deep LearningLess is More: Fewer Interpretable Region via Submodular Subset Selection
Image attribution algorithms aim to identify important regions that are highly relevant to model decisions. Although existing attribution solutions can effectively assign importance to target elements, they still face th…
Error UnderstandingImage AttributionInterpretability Techniques for Deep Learning