Feature Visualization

Chris Olah; Alexander Mordvintsev; Ludwig Schubert

doi:10.23915/distill.00007

journal article Nov 07, 2017

Feature Visualization

Chris Olah Alexander Mordvintsev Ludwig Schubert

Distill Vol. 2 No. 11 · Distill Working Group

View at Publisher Save 10.23915/distill.00007

Topics

No keywords indexed for this article. Browse by subject →

References

16

[1]

Erhan "Visualizing higher-layer features of a deep network" University of Montreal

[2]

Mordvintsev "Inceptionism: Going deeper into neural networks" Google Research Blog

[3]

Simonyan "Deep inside convolutional networks: Visualising image classification models and saliency maps" arXiv preprint arXiv:1312.6034

[4]

Gatys "A neural algorithm of artistic style" arXiv preprint arXiv:1508.06576

[5]

Mahendran "Understanding deep image representations by inverting them" 10.1109/cvpr.2015.7299155

[6]

Donglai Wei "Understanding Intra-Class Knowledge Inside {CNN}" CoRR

[7]

Nguyen "Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks" arXiv preprint arXiv:1602.03616

[8]

Nguyen "Plug \& play generative networks: Conditional iterative generation of images in latent space" arXiv preprint arXiv:1612.00005

[9]

Szegedy "Intriguing properties of neural networks" arXiv preprint arXiv:1312.6199

[10]

Bau "Network Dissection: Quantifying Interpretability of Deep Visual Representations" 10.1109/cvpr.2017.354

[11]

Odena "Deconvolution and checkerboard artifacts" Distill

[12]

Nguyen "Deep neural networks are easily fooled: High confidence predictions for unrecognizable images" 10.1109/cvpr.2015.7298640

[13]

Øygard "Visualizing GoogLeNet Classes"

[14]

Tyka "Class visualization with bilateral filters"

[15]

Mordvintsev "DeepDreaming with TensorFlow"

[16]

Nguyen "Synthesizing the preferred inputs for neurons in neural networks via deep generator networks"

Cited By

657

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI

Meike Nauta, Jan Trienes · 2023

ACM Computing Surveys

Deep-learning post-processing of short-term station precipitation based on NWP forecasts

Qi Liu, Xiao Lou · 2023

Atmospheric Research

Biometrics: Trust, But Verify

Anil K. Jain, Debayan Deb · 2022

IEEE Transactions on Biometrics, Be...

Explainable machine learning in materials science

Xiaoting Zhong, Brian Gallagher · 2022

npj Computational Materials

Interpretation and visualization techniques for deep learning models in medical imaging

Daniel T Huff, Amy J Weisman · 2021

Physics in Medicine and Biology

Automated Detection of Glaucoma With Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images

Parmita Mehta, Christine A. Petersen · 2021

American Journal of Ophthalmology

Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future

Grace W. Lindsay · 2021

Journal of Cognitive Neuroscience

Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer

Simon M. Thomas, James G. Lefevre · 2021

Medical Image Analysis

On Interpretability of Artificial Neural Networks: A Survey

Feng-Lei Fan, Jinjun Xiong · 2021

IEEE Transactions on Radiation and...

Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning

Amy McGovern, Ryan Lagerquist · 2019

Bulletin of the American Meteorolog...

Metrics

657

Citations

16

References

Details

Published: Nov 07, 2017
Vol/Issue: 2(11)

Authors

C

Chris Olah

Google Brain

A

Alexander Mordvintsev

Google Research

L

Ludwig Schubert

Google Brain

Cite This Article

Chris Olah, Alexander Mordvintsev, Ludwig Schubert (2017). Feature Visualization. Distill, 2(11). https://doi.org/10.23915/distill.00007

Feature Visualization

You May Also Like