mlsanityData to 7 Oct 2026

Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks

arXiv 2102.07799 ↗First posted 15 Feb 2021cs.CVcited by 11

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Abstract (arXiv)

Explainable AI (XAI) is an active research area to interpret a neural network's decision by ensuring transparency and trust in the task-specified learned models. Recently, perturbation-based model analysis has shown better interpretation, but backpropagation techniques are still prevailing because of their computational efficiency. In this work, we combine both approaches as a hybrid visual explanation algorithm and propose an efficient interpretation method for convolutional neural networks. Our method adaptively selects the most critical features that mainly contribute towards a prediction to probe the model by finding the activated features. Experimental results show that the proposed method can reduce the execution time up to 30% while enhancing competitive interpretability without compromising the quality of explanation generated.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 15 Feb 2021.

Builds on

13 of 21 references.

References · 13

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  3. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  4. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  5. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  6. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 936
  7. 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 825
  8. 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 575
  9. 2019Understanding Deep Networks via Extremal Perturbations and Smooth Maskscited by 257
  10. 2019Full-Gradient Representation for Neural Network Visualizationcited by 189
  11. 2014Object Detectors Emerge in Deep Scene CNNscited by 178
  12. 2020Restricting the Flow: Information Bottlenecks for Attributioncited by 110
1 more
  1. 2020Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregationcited by 13

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Cited by year

Citing papers per year: 2022: 3; 2023: 4; 2024: 2; 2025: 2.

11 papers.

  1. 2023Towards Better Explanations for Object Detectioncited by 14
  2. 2024LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Taskscited by 12
  3. 2023Towards Trust of Explainable AI in Thyroid Nodule Diagnosiscited by 10
  4. 2022Learning Visual Explanations for DCNN-Based Image Classifiers Using an Attention Mechanismcited by 7
  5. 2023TAME: Attention Mechanism Based Feature Fusion for Generating Explanation Maps of Convolutional Neural Networkscited by 6
  6. 2022ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention networkcited by 4
  7. 2024T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformerscited by 4
  8. 2025Context Attribution with Multi-Armed Bandit Optimizationcited by 2
  9. 2023On The Coherence of Quantitative Evaluation of Visual Explanationscited by 1
  10. 2022Rethinking gradient weights' influence over saliency map estimationcited by 0
  11. 2025P-TAME: Explain Any Image Classifier with Trained Perturbationscited by 0