Loss

Exploring the Landscape

Deep learning Explorers

MOVING LANDS

STILL LANDS

METHOD

Going deep

Latest update: November 17, 2019

  • Ongoing collaborations with researchers from MIT, NYU and other groups and institutions
  • New LL video visualization to be published soon
  • New gallery and video pages open for still and moving lands
  • To support the project, wall art and prints can now be acquired of some of the visualizations

In the intersection between research and art, the A.I LL project explores the morphology and dynamics of the fingerprints left by deep learning optimization training processes. The project goes deep into the training phase of these processes and generates high quality visualizations, using some of the latest deep learning and machine learning research and producing inspiring animations that can both inform and inspire the community. As the weight space changes through the optimization process, loss landscapes become alive, organic entities that challenge us to unlock the mysteries of learning. How do these multidimensional entities behave and change as we modify hyperparameters and other elements of our networks? How can we best tame these wild beasts as we cross their edge horizon on our way to the deepest convexity they hold?

LL is led by Javier Ideami, researcher, multidisciplinary creative director, engineer and entrepreneur. Contact Ideami on ideami@ideami.com

Mode Connectivity. Optima of complex loss functions connected by simple curves over which training and test accuracy are nearly constant. Visualization data generated through a collaboration between Pavel Izmailov (@Pavel_Izmailov), Timur Garipov (@tim_garipov) and Javier Ideami (@ideami). Based on the paper by timur garipov, pavel izmailov, dmitrii podoprikhin, dmitry vetrov, andrew gordon wilson: https://arxiv.org/abs/1802.10026 | creative visualization produced by Javier Ideami. This is part of an ongoing collaboration with Pavel and Timur, more results coming soon.

Just as a photograph converts the 3 dimensions of every day life into a 2 dimensional surface and interprets that 3D “reality” from a certain angle and perspective and through certain filters, loss landscape visualizations transform the multidimensional weight space of optimization processes into a much lower dimensional representation which we also process in different ways and study from a variety of angles and perspectives.

In both cases, even though we are simplifying the underlying “reality”, we are producing representations which provide useful information and may trigger new insights.

Through a combination of different tools and strategies, the loss landscape project samples hundreds of thousands of loss values across weight space and builds moving visualizations that capture some of the mysteries of the training processes of deep neural networks. In the intersection of technology, A.I and art, the LL project makes use of the cutting edge fast.ai library and the latest 3d proyection, animation and video production technology to produce pieces that take us on a journey into the unknown. 

Crafting the mission

The LL project crafting strategies are based on cutting edge artificial intelligence research combined with creative intuition. The mission is to explore the morphology and dynamics of these elusive creatures and inspire the community with visual pieces that make use of real data produced by deep learning training processes.

Every LL piece is carefully crafted with a combination of the finest tools and resources, from fast.ai to cutting edge 3D and movie production software. 

Phase 1 is now completed and Phase 2 is currently being prepared.

xyz

Going deep