This is the output of the neuralgae algorithm, which visualises a neural net's idea of some image classes and then classifies the result and repeats, looped into a GIF, and it's exactly the sort of fluid morphing between surreal forms I've been trying to get for two years.

Here are all 1000 classes in the ImageNet challenge, rendered with the Visual Geometry Group's 19-layer neural net, using the same deepdraw technique I used for the thousand faces of CaffeNet. These ones are rendered on random grayscale noise, not my face.

This is the net which the neuralgae Twitter …

I was tinkering around trying to get Justin Johnson's neural-style implementation working on the HPC - this is an algorithm which uses a deep learning image recognition net to extract low-level style features from one image and use them to render the large-scale contours of another. This technique has been commercialised …

I've got two short articles in MATERIALITY: FAKE, now online at Pinknantucket Press:

An uncanny valley in reverse is a brief illustrated attempt to explain the computational underpinning of deepdreams for a lay audience.

Vermiculation, on the use of fake rustic textures in classical architecture and its colonial domestic descendants …

I've updated the algorithm behind neuralgae. It's still the same basic principle: render a deepdraw image based on some random categories, feed the results back through the classifier, and then repeat with the new categories. But the draw part of the cycle now uses the weights from the classification stage …

I've made my first Twitter bot. It posts a new image and accompanying text from my Neuralgae NaNoGenMo project, six times a day, modified slightly so that it fits within the character limit. One of my goals with Neuralgae was to generate something which was in some sense readable, and …

For NaNoGenMo (national novel generation month) 2015, I wrote some Python which generates a series of loosely-coupled deepdream images, and adds eight-line surrealist 'poetry' to them based on the image categories used to draw them.

The full text is here: Neuralgae.

The source code and a fairly detailed description of …

Here are visualisations of the 205 scene categories in the MIT Places Database using a variant of the same technique used to generate the thousand faces of Caffenet.

These are rendered on random grayscale noise rather than a photograph, so they're purely a manifestation of what the neural net has …

Here are the thousand image categories in the ImageNet Challenge data visualisation context, rendered onto a webcam photo of me using the deepdraw technique.

This is the set of image classifications which the standard Deepdreams imagery is based on: as more than one hundred of the categories are dog breeds …