mlsanityData to 7 Oct 2026

GAM: Explainable Visual Similarity and Classification via Gradient Activation Maps

arXiv 2109.00951 ↗First posted 2 Sep 2021cs.CV cs.AI cs.LGcited by 8

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Abstract (arXiv)

We present Gradient Activation Maps (GAM) - a machinery for explaining predictions made by visual similarity and classification models. By gleaning localized gradient and activation information from multiple network layers, GAM offers improved visual explanations, when compared to existing alternatives. The algorithmic advantages of GAM are explained in detail, and validated empirically, where it is shown that GAM outperforms its alternatives across various tasks and datasets.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 24 of its 38 references that are in the map. First posted on arXiv 2 Sep 2021.

Builds on

24 of 38 references.

References · 24

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  11. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 936
  12. 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 459
12 more
  1. 2015Deep Metric Learning via Lifted Structured Feature Embeddingcited by 449
  2. 2014Deep metric learning using Triplet networkcited by 421
  3. 2020Asymmetric Loss For Multi-Label Classificationcited by 379
  4. 2015Understanding Neural Networks Through Deep Visualizationcited by 332
  5. 2014Learning Fine-grained Image Similarity with Deep Rankingcited by 189
  6. 2014Deep Learning Face Representation by Joint Identification-Verificationcited by 162
  7. 2015Particular object retrieval with integral max-pooling of CNN activationscited by 160
  8. 2015Visualizing Deep Convolutional Neural Networks Using Natural Pre-Imagescited by 93
  9. 2016CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examplescited by 58
  10. 2019Visualizing Deep Similarity Networkscited by 24
  11. 2020Adapting Grad-CAM for Embedding Networkscited by 21
  12. 2014Visualizing and Comparing Convolutional Neural Networkscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 0; 2025: 2; 2026: 1.

8 papers.

  1. 2022Grad-SAM: Explaining Transformers via Gradient Self-Attention Mapscited by 42
  2. 2023Visual Explanations via Iterated Integrated Attributionscited by 18
  3. 2023Deep Integrated Explanationscited by 9
  4. 2022Interpreting BERT-based Text Similarity via Activation and Saliency Mapscited by 8
  5. 2023Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Modelscited by 3
  6. 2025Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanationscited by 2
  7. 2026Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustnesscited by 2
  8. 2025Do Protein Transformers Have Biological Intelligence?cited by 0