Index Catalog // ScholarsArchive@OSU

Creator:: Ratzlaff, Neale
Abstract:: The ability to extract uncertainties from predictions is crucial for the adoption of deep learning systems to safety-critical applications. Uncertainty estimates can be used as a failure signal, which is necessary for automating complex tasks where safety is a concern. Furthermore, current deep learning systems do not provide uncertainty estimates,...
Resource Type:: Dissertation

Creator:: Neal, Lawrence W.
Abstract:: In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instances of existing classes, this thesis introduces a dataset augmentation technique called counterfactual image generation. This approach, based on...
Resource Type:: Masters Thesis
Full Text:: the IEEE conference on computer vision and pattern recognition, pages 1563–1572, 2016. [3] David

Creator:: Kaur, Damanpreet
Abstract:: The abilities of plant biologists to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation and mainly to collect this data on a high-throughput scale at low cost. Deep learning-based methods have demonstrated unprecedented potential to automate...
Resource Type:: Masters Thesis

Creator:: Li, Xingyi
Abstract:: The performance of deep learning frameworks could be significantly improved through considering the particular underlying structures for each dataset. In this thesis, I summarize our three work about boosting the performance of deep learning models through leveraging structures of the data. In the first work, we theoretically justify that, for...
Resource Type:: Dissertation

Creator:: Khorram, Saeed
Abstract:: In this thesis, we introduce a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by embedding a high-dimensional activation vector of a deep network layer non-linearly into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can...
Resource Type:: Masters Thesis

Creator:: Firl, Alrik K.
Abstract:: This thesis consists of two major components. The first part is concerned with video object instance segmentation (VOS), which is the task of assigning per-pixel labels perframe of a video sequence to indicate foreground object instance membership, given the first frame ground truth mask. VOS has myriad applications, from video...
Resource Type:: Masters Thesis

Creator:: Wu, Wenxuan
Abstract:: As one of the most popular data types, the point cloud is widely used in various appli- cations, including computer vision, computer graphics and robotics. The capability to directly measure 3D point clouds is invaluable in those applications as depth information could remove a lot of the segmentation ambiguities in...
Resource Type:: Dissertation

Creator:: DeBortoli, Robert A.
Abstract:: Deep learning has recently revolutionized robot perception in many canonical robotic applications, such as autonomous driving. However, a similar transformation has yet to occur in more harsh environments including underwater and underground. This is due in part to the difficulty in deploying robots in these environments, which lack large real...
Resource Type:: Dissertation

Creator:: Yuan, Jialin
Abstract:: Learning to recognize objects is a fundamental and essential step in human perception and understanding of the world. Accordingly, research of object discovery across diverse modalities plays a pivotal role in the context of computer vision. This field not only contributes significantly to enhancing our understanding of visual information but...
Resource Type:: Dissertation

ScholarsArchive@OSU