MindsEye

DeepArtist.org is intended to be the umbrella for artistic-themed applications built using MindsEye. This site currently consists of examples.deepartist.org which displays example notebooks provided for this project.

• github://deepartist.org
• github://examples.deepartist.org
• Release 2.0.0

Hello Everybody!

Today I’d like to announce the 2.0.0 release of MindsEye and related projects!

It’s been quite a journey already…

MindsEye is an AI framework built using Java. It uses reference counting for efficient resource use, and uses libraries such as CuDNN (CUDA) and Aparapi (OpenCL) to do numerical heavy lifting. It provides a highly customizable optimization library, and a wide variety of pre-coded layers.

• GitHub URLs
• Release 2.0.0

I’m pleased today to announce the release of the Simiacryptus data tools v1.8.0, including the first version of a new image art publishing application named and located at DeepArtist.org - Notably using the subdomain examples.deepartist.org. What is it? DeepArtist.org is an image processing platform using convolutional neural networks to perform state-of-the-art image processing techniques. This software is targeted at hobbyists and digital artists, and as such this documentation is focused on the practical tools provided to produce pretty pictures.

Our newest improvements to style transfer have improved results even further, with better quality results that are both artistically more distinctive and more recognizable in content. The first improvement used an additional color transformation step to remove color differences before style transfer, and re-add the difference afterwards using an inverted transformation; without it, the best results would require the style and content inputs have similar color schemes. The second improvement is much more involved, but essentially involves identifying regions of a given image.

Let’s say you have a local java application you are developing. For some reason, you want to run some code on AWS EC2 - After all, the cloud and virtual computing revolution makes all that theoretically easy. All you need is an AWS account… right? However, if you are starting from a local Java application and just have the goal “run this code on the cloud”, there are actually quite a few problems to solve before you can do this.

One very entertaining application of deep learning is in style modification and pattern enhancement, which has become a popular topic on the internet after Google’s Deep Dream post and subsequent research and publications on style transfer. Reproducing this research has long been a goal for the development of MindsEye, and now that it is achieved I’m having quite a bit of fun with this playground I built! I have collected the interesting visual results of my work in this online album.

Deep Learning has in recent years seen dramatic success in the field of computer vision. Deep convolutional neural networks tens of layers deep are becoming common and are some of the best performers for image recognition. Additionally, these learned networks can be used to produce novel artwork, as seen in recent publications about Deep Dream and Style Transfer. Today we will explore these applications with our own neural network platform, MindsEye.

Hello! Today we will be discussing many aspects of developing differentiable network layers in MindsEye as we explore the 2d convolution layer and its various implementations. First, for background, see my previous post about Test Driven Development with neural networks. Given these test facilities and perhaps more elemental layers, we need to construct a convolution layer that will work in large modern networks with large images as input. Our first goal is to code a reference implementation, generally in pure java.