Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images
Paper summary This paper is about finding naturally looking images for the analysis of machine learning models in computer vision. There are 3 techniques: * **inversion**: the aim is to reconstruct an image from its representation * **activation maximization**: search for patterns that maximally stimulate a representation component (deep dream). This does NOT use an initial natural image. * **caricaturization**: exaggerate the visual patterns that a representation detects in an image The introduction is nice. ## Code The paper comes with code: [robots.ox.ac.uk/~vgg/research/invrep](http://www.robots.ox.ac.uk/~vgg/research/invrep/index.html) ([GitHub: aravindhm/deep-goggle](https://github.com/aravindhm/deep-goggle)) ## Related * 2013, Zeiler & Fergus: [Visualizing and Understanding Convolutional Networks ](http://www.shortscience.org/paper?bibtexKey=journals/corr/ZeilerF13#martinthoma)
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Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images
Aravindh Mahendran and Andrea Vedaldi
arXiv e-Print archive - 2015 via arXiv
Keywords: cs.CV, 68T45

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