A Neural Algorithm of Artistic Style

Welcome to the DC/NoVA Papers We Love meetup!

Papers We Love is an international organization centered around the appreciation of computer science research papers. There's so much we can learn from the landmark research that shaped the field and the current studies that are shaping our future. Our goal is to create a community of tech professionals passionate about learning and sharing knowledge. Come join us!

New to research papers? Watch The Refreshingly Rewarding Realm of Research Papers (https://www.youtube.com/watch?v=8eRx5Wo3xYA) by Sean Cribbs.

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// Tentative Schedule

• 7:00-7:30–Informal paper discussion

• 7:30-7:35–Introduction and announcements

• 7:35-8:40–A Neural Algorithm of Artistic Style (https://arxiv.org/abs/1508.06576), presented by Valerie Woolard Srinivasan

• 8:40-9:00–Informal paper discussion

// Directions

CustomInk Cafe (3rd Floor)
Mosaic District, 2910 District Ave #300
Fairfax, VA 22031

When you get here you can come in via the patio. Don't be scared by the metal gate and sign. It's accessible via the outside stairs near True Food. There is a parking garage next door for those coming by vehicle. And, there is a walkway to the patio on the 3rd floor of the garage nearest moms organic market.

Metro: The Dunn Loring metro station is about 0.7 miles from our meetup location. It’s very walkable, but if you’d prefer a bus, the 402 Southbound and 1A/1B/1C Westbound leave from Dunn Loring Station about every 5-10 minutes (see a schedule for more detailed timetable).

If you're late, we totally understand–please still come! (via the patio is best) Just be sure to slip in quietly if a speaker is presenting.

// Paper

A Neural Algorithm of Artistic Style by Leon Gatys, Alexander Ecker, and Matthias Bethge

site (https://arxiv.org/abs/1508.06576) | pdf (https://arxiv.org/pdf/1508.06576.pdf)

https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/77c512cbb832436e1a35ad434e6bb3d763799763/4-Figure2-1.png

"In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery."