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
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
“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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