Understanding Neural Networks Through Deep Visualization
Understanding Neural Networks Through Deep Visualization Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson Quick links: ICML DL Workshop paper | code | video These images are synthetically generated to maximally activate individual neurons in a Deep Neural Network (DNN). They show what each neuron “wants to see”, and thus what each neuron has learned to look for. The neurons sel
GitHub - yosinski/deep-visualization-toolbox: DeepVis Toolbox
The main toolbox window looks like this, here showing a convolutional unit that responds to automobile wheels: For a quick tour of the toolbox features, including what each pane of the above interface is showing, watch this 4 min YouTube video. In addition to processing images files from disk, the toolbox can run off a webcam for live network visualization (below left). The toolbox comes bundled w
Making Visual Data a First-Class Citizen
“Above all, don't lie to yourself. The man who lies to himself and listens to his own lie comes to a point that he cannot distinguish the truth within him, or around him, and so loses all respect for himself and for others. And having no respect he ceases to love.” ― Fyodor Dostoyevsky, The Brothers Karamazov City Forensics: Using Visual Elements to Predict Non-Visual City Attributes To respect th
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GitHub - streamlit/demo-self-driving: Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.
