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Lottery visualizes from above and from the side the performance of a Resnet18 (Mnist dataset) as the weights of the network are gradually being pruned (based on arxiv:1803.03635 by jonathan frankle, michael carbin).
Up to 80% pruning it can be observed in this specific network that the performance of the retrained networks with pruned weights can equal or exceed the original one when evaluating the test dataset.
Loss Landscape generated with real data: resnet18 / mnist, sgd-adam, bs=60, lr sched, eval mod, log scaled (orig loss nums) & vis-adapted.

This piece has been created through a multi day process composed of different stages that begin with the training of deep learning neural networks and proceeds through a number of phases until arriving to the final artwork.

When analyzing the loss landscape generated while increasing dropout, we see a noise layer taking over the landscape, a layer that is disruptive enough to help in preventing overfitting and the m...

Explorer of A.I, AR, advanced visualizations & others, multidisciplinary engineer & creative director. As a multidisciplinary creative director, engineer, artist and entrepreneur, Javier has travelled the world leading projects, exhibiting his work and giving talks, from Silicon Valley to the jungles of Bali, from Stanford University, through the United Nations FAO HQ and the financial center of London, to the International Cultural Diplomacy Conference in Berlin and many others. In his latest works, Javier Ideami produces A.I artworks through a complex process that can last many days. The process begins with training a neural network and capturing relevant data. This multidimensional data is then transformed into a 3D piece which is...