622 citations · 791 across the 7 of their papers we have counts for
5 papers · 1 filter
Self-Supervised Relative Depth Learning for Urban Scene Understanding
Huaizu Jiang, Erik Learned-Miller, Gustav Larsson +2
As an agent moves through the world, the apparent motion of scene elements is (usually) inversely proportional to their depth. It is natural for a learning agent to associate image…
Discovery of Visual Semantics by Unsupervised and Self-Supervised Representation Learning
Gustav Larsson
The success of deep learning in computer vision is rooted in the ability of deep networks to scale up model complexity as demanded by challenging visual tasks. As complexity is inc…
Colorization as a Proxy Task for Visual Understanding
Gustav Larsson, Michael Maire, Gregory Shakhnarovich
We investigate and improve self-supervision as a drop-in replacement for ImageNet pretraining, focusing on automatic colorization as the proxy task. Self-supervised training has be…
FractalNet: Ultra-Deep Neural Networks without Residuals
Gustav Larsson, Michael Maire, Gregory Shakhnarovich
We introduce a design strategy for neural network macro-architecture based on self-similarity. Repeated application of a simple expansion rule generates deep networks whose structu…
Learning Representations for Automatic Colorization
Gustav Larsson, Michael Maire, Gregory Shakhnarovich
We develop a fully automatic image colorization system. Our approach leverages recent advances in deep networks, exploiting both low-level and semantic representations. As many sce…