“Image Synthesis from Yahoo’s open_nsfw”, Gabriel Goh2016 (CNN; backlinks)⁠:

Yahoo’s recently open sourced neural network, open_nsfw, is a fine tuned Residual Network which scores images on a scale of 0 to 1 on its suitability for use in the workplace…What makes an image NSFW, according to Yahoo?

I explore this question with a clever new visualization technique by Nguyen et al…Like Google’s Deep Dream, this visualization trick works by maximally activating certain neurons of the classifier. Unlike deep dream, we optimize these activations by performing descent on a parameterization of the manifold of natural images.

[Demonstration of an unusual use of backpropagation to ‘optimize’ a neural network: instead of taking a piece of data to input to a neural network and then updating the neural network to change its output slightly towards some desired output (such as a correct classification), one can instead update the input so as to make the neural net output slightly more towards the desired output.

When using an image classification neural network, this reversed form of optimization will ‘hallucinate’ or ‘edit’ the ‘input’ to make it more like a particular class of images.

In this case, a porn/NSFW-detecting NN is reversed so as to make images more (or less) “porn-like”. Goh runs this process on various images like landscapes, musical bands, or empty images; the maximally/minimally porn-like images are disturbing, hilarious, and undeniably pornographic in some sense.]

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