Novel Image Representations and Learning Tasks
|Abstract||Computer Vision as a eld has gone through signicant changes in the last decade.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, gen... (more)
|Contributor||Venkatesan, Ragav (Author) / Li, Baoxin (Advisor) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)|
|Subject||Computer science / Dataset Generality / Deep Learning / Image Representations / Mentee Networks / Multiple Instance Learning|
|Reuse Permissions||All Rights Reserved|
|Note||Doctoral Dissertation Computer Science 2017|
|Collaborating Institutions||Graduate College / ASU Library|
|Additional Formats||MODS / OAI Dublin Core / RIS|