Haiku Tech Center

 Does it really? Let's avoid a discussion of what 'works best' even means, let alone 'style'.  For now. I grabbed this archived discussion from reddit and copy/pasted it here below in case the one on reddit vanishes for some reason.  And it's a very interesting read, and it highlights some things we kept pointing out at HTC in many previous posts.  That there is something about the VGG architecture that seems to work well with a number of different neural net image transformation tasks. An acquaintance a year or two ago was messing around with neural style transfer ( Gatys et al 2016 ), experimenting with some different approaches, like a tile-based GPU implementation for making large poster-size transfers, or optimizing images to look different using a two-part loss: one to encourage being like the style of the style image, and a negative one to penalize having content like the source image; this is unstable and can diverge, but when it works, looks cool. (Exa...

How can artists become more directly involved in working with and controlling different deep learning based style transfer techniques?  This is an important consideration, as many deep learning systems and algorithms are developed by computer scientists or engineers, who oftentimes have very different sensibilities than people trained with a traditional art background (artists) . Several different approaches were presented and discussed at Siggraph 2020.  Let's take a look at two of them that were presented at the Real-Time Live demo session.  Both of these are from sections of a much longer video that presents a wide variety of different live demos, and while the rest of the longer video does not directly relate to today's post topic, feel free to watch them if you are interested. The first live demo is called 'Interactive Video Stylization Using Few-Shot Patch-Based Training' by Ondrej Texler. The second live demo is called 'Sketch-To-Art: Synthesizing Stylized Ar...