Differentiable Programming for Image Processing and Deep Learning in Halide
Abstract Gradient-based optimization has enabled dramatic advances in computational imaging through techniques like deep learning and nonlinear optimization. These methods require gradients not just of simple mathematical functions, but of general programs which encode complex transformations of images and graphical data. Unfortunately, practitioners have traditionally been limited to either hand-
Video Magnification
Many seemingly static scenes contain subtle changes that are invisible to the naked human eye. However, it is possible to pull out these small changes from videos through the use of algorithms we have developed. We give a way to visualize these small changes by amplifying them and we present algorithms to pull out interesting signals from these videos, such as the human pulse, sound from vibrating
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
http://people.csail.mit.edu/beenkim/papers/KIM2016NIPS_MMD.pdf
http://people.csail.mit.edu/donglai/paper/chang13_CVPR.pdf
http://people.csail.mit.edu/dannyf/traj.pdf
http://people.csail.mit.edu/haitham/Papers/a12-shi.pdf
http://people.csail.mit.edu/jchang7/pubs/publications/chang14_ECCV.pdf
