Building Constraints, Geometric Invariants and Interpretability in Deep Learning: Applications in Computational Imaging and Vision
abstract: Over the last decade, deep neural networks also known as deep learning, combined with large databases and specialized hardware for computation, have made major strides in important areas such as computer vision, computational imaging and natural language processing. However, such framework...
Other Authors: | Lohit, Suhas Anand (Author) |
---|---|
Format: | Doctoral Thesis |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | http://hdl.handle.net/2286/R.I.55542 |
Similar Items
-
Deep learning in computer vision: A critical review of emerging techniques and application scenarios
by: Junyi Chai, et al.
Published: (2021-12-01) -
CloudCV: Deep Learning and Computer Vision on the Cloud
by: Agrawal, Harsh
Published: (2016) -
Interpretable Machine Learning and Sparse Coding for Computer Vision
by: Landecker, Will
Published: (2014) -
Deep Parameter Selection For Classic Computer Vision Applications
by: Whitney, Michael
Published: (2021) -
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks
by: Jordan R. Ubbens, et al.
Published: (2017-07-01)