Activation Atlas
Open accessAbstract
By using feature inversion to visualize millions of activations from an image classification network, we create an explorable activation atlas of features the network has learned and what concepts it typically represents.
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One of the key areas of current research in AI is understanding decision making inside the neural network.
“One of the key areas of current research in AI is understanding decision making inside the neural network (Carter et al, 2019).”
From Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2021) · cites this paper as Carter et al, 2019
The activation atlas of an AI model correlates the internal neural net nodes into features, and a visual overlay of the features helps improve understanding of the AI decision making process.
“The activation atlas of an AI model correlates the internal neural net nodes into features, and a visual overlay of the features help improve our understanding of the AI decision making process.”
From Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2021) · cites this paper as Carter et al, 2019
Cites
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works
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Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · see citing works
Explainable artificial intelligence: a comprehensive review
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983 · see citing works
Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts
Zamfirescu-Pereira, J. D., Wong, R. Y., Hartmann, B., & Yang, Q. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581388 · Cited by 893 · see citing works
“However, we did validate BotDesigner’s functionality and usability throughout this workfow via a set of pilot user studies (see Appendix A.3 for details) to ensure that these pieces are available should users choose to systematically assess their prompt designs.”
MethodA Critical Review of Machine Learning of Energy Materials
Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609 · see citing works
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Shmatko, A., Ghaffari Laleh, N., Gerstung, M., & Kather, J. N. (2022) · Nature Cancer · doi:10.1038/s43018-022-00436-4 · Cited by 446 · see citing works
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., Khlaaf, H., Yang, J., Toner, H., Fong, R., et al. (2020) · arXiv · doi:10.48550/arxiv.2004.07213 · Cited by 306 · see citing works
Zoom In: An Introduction to Circuits
Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M., & Carter, S. (2020) · Distill · doi:10.23915/distill.00024.001 · Cited by 288 · see citing works
Self-citationArtificial Intelligence in Dermatology: A Primer
Young, A. T., Xiong, M., Pfau, J., Keiser, M. J., & Wei, M. L. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.02.026 · Cited by 249 · see citing works
Kaissis, G., Ziegelmayer, S., Lohöfer, F., Algül, H., Eiber, M., Weichert, W., Schmid, R., Friess, H., Rummeny, E., Ankerst, D., et al. (2019) · European Radiology Experimental · doi:10.1186/s41747-019-0119-0 · Cited by 79 · see citing works
Establishing and evaluating trustworthy AI: overview and research challenges
Kowald, D., Scher, S., Pammer-Schindler, V., Müllner, P., Waxnegger, K., Demelius, L., Fessl, A., Toller, M., Mendoza Estrada, I. G., Šimić, I., et al. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1467222 · Cited by 59 · see citing works
VisQA: X-raying Vision and Language Reasoning in Transformers
Jaunet, T., Kervadec, C., Vuillemot, R., Antipov, G., Baccouche, M., & Wolf, C. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114683 · Cited by 30 · see citing works
“More recently, CNNs have been analyzed through the prism of attribution maps in works such as Activation-atlas [10] and attribution graphs [26].”
Bur, A. M., Zhang, T., Chen, X., Kavookjian, H., Kraft, S., Karadaghy, O., Farrokhian, N., Mussatto, C., Penn, J., & Wang, G. (2023) · Otolaryngology–Head and Neck Surgery · doi:10.1002/ohn.411 · Cited by 25 · see citing works
- Authors
- Carter, S., Armstrong, Z., Schubert, L., Johnson, I., & Olah, C.
- Affiliations
- Google (United States) (US)
- Google Accelerated Science
- OpenAI (United States) (US)
- Google Cloud
- Venue
- Distill
- Publisher
- Distill Working Group
- Published
- 2019-03-06
- Volume
- 4
- Issue
- 3
- ISSN
- 2476-0757 (ISSN-L)
- OpenAlex
- W4253525579
- Cited by
- 147
- References
- 1
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 20 · 2023: 24 · 2022: 24 · 2021: 30 · 2020: 19 · 2019: 12
- FWCI
- 9.81
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Distill (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- activation visualization, neural network interpretability, learned features, concept visualization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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