The Loss Landscape high resolution collection pieces are created with real data from the training processes of artificial deep neural networks (AI) through a complex process composed of multiple stages and contain the highest resolution and quality representations in the world of the surfaces that express the performance of the learning processes of artificial neural networks. In these pieces you are looking at the fingerprints of the learning process of artificial machines, the shadows of spaces that have billions of dimensions, spaces navigated by neural networks that are looking for the right combination of their parameters. To create this piece I slice the very high dimensional space to capture a dimensionality reduced representation that preserves the essence of its key features, combining engineering, scientific and creative disciplines to produce, through a number of complex stages, the final piece in very high detail and resolution, in a process that requires a lot of time, computation, effort and creativity. Because of the difficulty of the creation of these pieces in high resolution and detail, there are very few available.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 NeurIPS 2018 paper by Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, Andrew Gordon Wilson: https://arxiv.org/abs/1802.10026 | Creative visualization and artwork produced by Javier Ideami. This artwork shows the area of the two minima and also higher loss areas which contain irregular morphology. Real data, resnet-20 no-skip, imagenette, sgd-mom, bs=128wd=3e-4, mom=0.9, bn, train mod, 1 million pts, log scaled (orig loss nums)