Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 23 for “"Weakly Supervised Learning"”.
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Weakly supervised learning from referring expression: Challenge and directions
We explore methods of weakly supervised learning from referring expression. Unlike traditional fully supervised semantic segmentation of object recognition tasks, in which a a small set of discrete class bases is provided, the referring expression task is performed associated with a sentence …
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A study of remote sensing based natural and built environment monitoring: from fully supervised to weakly supervised learning
… advantages and usage by employing the classical supervised learning methodology. Through this practice, the operational readiness of adopting optical RS technologies for flood disaster management is analyzed, and technical challenges are identified. In addition, I fill a technical gap in past RS …
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Learning with Constraint-Based Weak Supervision
Recent adaptations of machine learning models in many businesses has underscored the need for quality training data. Typically, training supervised machine learning systems involves using large amounts of human-annotated data. Labeling data is expensive and can be a limiting factor in using machine …
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Feedback convolutional neural network in applications of computer vision
… deeper and wider, the demand for high quality supervised training data also increases exponentially with the model complexity. Considering the difficulty in data acquisition of high quality and complete labels, the topic of weakly-supervised learning raises much attention recently in both …
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Modeling Structured Data with Invertible Generative Models
… intelligence. Generative models are machine learning models, which model datasets with probability distributions. Deep generative models combine deep learning with probability theory, so that can model complicated datasets with flexible models. They have become one of the most popular models …
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Learning without Expert Labels for Multimodal Data
While advancements in deep learning have been largely possible due to the availability of large-scale labeled datasets, obtaining labeled datasets at the required granularity is challenging in many real-world applications, especially in scientific domains, due to the costly and labor-intensive …
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Overcoming resource limitations in the processing of unlimited speech : applications to speaker and language recognition
… data. In particular, successful methods in unsupervised domain adaptation can automatically recognize and adapt existing algorithms to systematic changes in the input. Furthermore, methods that can organize incoming streams of information can allow us to derive insights with minimal manual …
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Enhancing dimensionality in remote sensing images
… is computationally prohibitive. As a solution, learning-based approaches are employed to reformulate the tasks as supervised or weakly supervised learning problems. These methods leverage data-driven models to approximate the mappings, offering practical and scalable solutions for dimensionality …
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Learning Object Detection with Weak Supervision
Deep learning technique has achieved astonishing success in many computer vision applications. However, training deep models typically requires large-scale datasets with elaborate annotations. Collecting and annotating large-scale datasets are laborious, especially for object detection --- a …
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Visual feature learning with application to medical image classification
… this work, I give emphasis on learning highly discriminative local features and image representations to achieve the best possible classification performance for medical images, particularly for colonoscopy and histology (cell) images. I propose approaches to learn local …
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Knowledge-Informed Weakly-Supervised Deep Learning Models for Cancer Applications
In recent decades, deep learning (DL) has emerged as a powerful tool for analyzing complex patterns in large-scale healthcare data, significantly advancing diagnosis, prognosis, and treatment planning. However, the collection of medical data faces inherent limitations, including invasiveness, high …
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Learning with Weak Supervision for Land Cover Mapping Problems
… time. This coupled with the advances in machine learning and high performance computing provide an opportunity to automate the land cover mapping problem at scale. However, the availability of labeled data to train predictive models in this application is very limited, especially in the …
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Recognition of Human Activities in Hybrid Teamwork with Industrial Robot Systems
… cross-modality fusion of unknown sources, and weakly-supervised learning from unprepared samples. For the evaluation and training, generic datasets are used to improve transferability, along with realistic and limited domain-specific datasets to prove applicability. The finally introduced …
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Robust Deep Learning Methodologies for Weakly Supervised Remote Sensing Image Classification
… studies and environmental monitoring. Deep learning (DL) has proven very effective in addressing the analytical challenges posed by this data, excelling in image analysis and sequential data processing. However, in remote sensing (RS), DL is often hindered by scarce and imperfect labeled …
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Weakly-supervised text classification
… many real-world applications. Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this work, we propose a weakly-supervised framework that addresses the lack of …
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Text Prompt-Driven Medical Image Segmentation
… multimodal segmentation methods targeting fully supervised and weakly supervised settings, with each method addressing specific bottlenecks. Our main contribution lies in the use of text-driven guidance to solve the respective challenges: computational inefficiency in full supervision and …
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Scene Monitoring With A Forest Of Cooperative Sensors
… novel video understanding algorithms with learning capability, to detect and categorize people and vehicles, track them with in a camera and hand-off this information across multiple networked cameras for multi-camera tracking. The ability to learn prevents the need for extensive manual …
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Integrating local context and global cohesiveness for open information extraction
Extracting entities and their relations from text is an important task for understanding massive text corpora. Open information extraction (IE) systems mine relation tuples (i.e., entity arguments and a predicate string to describe their relation) from sentences, and do not confine to a pre-defined …
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Reframing Cox Proportional Hazards Model for Big Data and Neural Networks
… labels. For example, we see this with typical weakly supervised learning methods, MIL, and attention-based MIL. A WSI typically has hundreds of thousands of image patches, each of which may carry different information about the slide label/class. Training a deep neural network with thousands of …
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Advanced Deep Learning Methods for the Automatic Analysis of Radar Sounder Data
… availability of labeled examples. While Deep Learning (DL) has revolutionized image analysis in many domains, its application to RS is limited by the scarcity of labeled data, the presence of different noise sources, and the uncommon characteristics of the data. This limits the effectiveness …
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