Differentiable programming for image processing and deep learning in halide
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 g...
Main Authors: | Li, Tzu-Mao, Gharbi, Michael Yanis, Adams, Andrew, Durand, Frederic, Ragan-Kelley, Jonathan |
---|---|
Other Authors: | Massachusetts Institute of Technology. Laboratory for Computer Science |
Format: | Article |
Published: |
Association for Computing Machinery (ACM)
2019
|
Online Access: | https://hdl.handle.net/1721.1/122623 |
Similar Items
-
Differentiable Vector Graphics Rasterization for Editing and Learning
by: Li, Tzu-Mao, et al.
Published: (2025) -
Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines
by: Barnes, Connelly, et al.
Published: (2014) -
Learning efficient image processing pipelines
by: Gharbi, Michael (Michael Yanis)
Published: (2019) -
A Dataset of Multi-Illumination Images in the Wild
by: Murmann, Lukas, et al.
Published: (2021) -
Designing Perceptual Puzzles by Differentiating Probabilistic Programs
by: Chandra, Kartik, et al.
Published: (2022)