mlsanityData to 7 Oct 2026

Interactive Visual Feature Search

arXiv 2211.15060 ↗First posted 28 Nov 2022cs.CVcited by 0

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Abstract (arXiv)

Many visualization techniques have been created to explain the behavior of computer vision models, but they largely consist of static diagrams that convey limited information. Interactive visualizations allow users to more easily interpret a model's behavior, but most are not easily reusable for new models. We introduce Visual Feature Search, a novel interactive visualization that is adaptable to any CNN and can easily be incorporated into a researcher's workflow. Our tool allows a user to highlight an image region and search for images from a given dataset with the most similar model features. We demonstrate how our tool elucidates different aspects of model behavior by performing experiments on a range of applications, such as in medical imaging and wildlife classification.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 41 of its 56 references that are in the map. First posted on arXiv 28 Nov 2022.

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References · 41

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  5. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  6. 2016Densely Connected Convolutional Networkscited by 6,691
  7. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  8. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  9. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  10. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  11. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  12. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
29 more
  1. 2019Natural Adversarial Examplescited by 1,401
  2. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,366
  3. 2018Sanity Checks for Saliency Mapscited by 1,149
  4. 2017SmoothGrad: removing noise by adding noisecited by 1,097
  5. 2019Learning Robust Global Representations by Penalizing Local Predictive Powercited by 1,010
  6. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 969
  7. 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 825
  8. 2017Network Dissection: Quantifying Interpretability of Deep Visual Representationscited by 703
  9. 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 611
  10. 2014Understanding Deep Image Representations by Inverting Themcited by 399
  11. 2016Generative Visual Manipulation on the Natural Image Manifoldcited by 348
  12. 2015Understanding Neural Networks Through Deep Visualizationcited by 332
  13. 2016Top-down Neural Attention by Excitation Backpropcited by 318
  14. 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
  15. 2020Semantic Photo Manipulation with a Generative Image Priorcited by 178
  16. 2021TorchXRayVision: A library of chest X-ray datasets and modelscited by 131
  17. 2015Visualizing Deep Convolutional Neural Networks Using Natural Pre-Imagescited by 93
  18. 2020The iWildCam 2020 Competition Datasetcited by 86
  19. 2021A Study of Face Obfuscation in ImageNetcited by 83
  20. 2020A Decade Survey of Content Based Image Retrieval using Deep Learningcited by 72
  21. 2021Deep Learning for Instance Retrieval: A Surveycited by 70
  22. 2016SIFT Meets CNN: A Decade Survey of Instance Retrievalcited by 66
  23. 2021Editing a classifier by rewriting its prediction rulescited by 64
  24. 2020There and Back Again: Revisiting Backpropagation Saliency Methodscited by 60
  25. 2020A Framework For Contrastive Self-Supervised Learning And Designing A New Approachcited by 51
  26. 2021PASS: An ImageNet replacement for self-supervised pretraining without humanscited by 51
  27. 2017Direct-Manipulation Visualization of Deep Networkscited by 26
  28. 2022VisCUIT: Visual Auditor for Bias in CNN Image Classifiercited by 8
  29. 2017Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learningcited by 7

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