{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129216"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129216","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Adaptive deep learning under data scarcity","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Dou Hoon Kwark, accepted the attached license on 2025-04-17 at 12:29.","The student, Dou Hoon Kwark, submitted this Thesis for approval on 2025-04-17 at 12:30.","This Thesis was approved for publication on 2025-04-17 at 13:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21822 on 2025-10-19 at 18:09:31","Deep learning has catalyzed transformative breakthroughs in computer vision and related fields, but these advances often rely on large-scale datasets that are neither readily accessible nor cost-effective in many real-world contexts. In many practical domains—such as medical imaging and geological mapping—collecting large-scale, expertly annotated datasets is prohibitively expensive. This thesis investigates a range of strategies designed to alleviate the pervasive challenge of data scarcity. Through comprehensive studies on both discriminative and generative tasks—including segmentation, super-resolution, modality translation, and inpainting—we demonstrate novel frameworks that preserve strong predictive performance despite limited training data. Our central goal is to show that deep learning under constrained data can still deliver robust results, provided the modeling pipelines are carefully adapted to the problem at hand. First, we propose a hierarchical diffusion-based approach that synthesizes pseudo-healthy medical images with enhanced 3D consistency but moderate computational overhead. Second, we present a fusion strategy that integrates multiple 2D diffusion models into a lightweight 3D representation, improving volumetric realism when data points are limited. Finally, we explore a multi-encoder pipeline that leverages color-space transformations to better segment complex maps, demonstrating its utility in settings like geological digitization. Taken together, these contributions illustrate that addressing data scarcity does not require sacrificing performance. Rather, it calls for more nuanced model design—incorporating domain-specific insights, ensemble architectures, and complementary data transformations. Our experiments show consistent improvements across diverse tasks, highlighting the promise of deep learning solutions that are nimble enough to excel in resource-constrained environments."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Adaptive deep learning under data scarcity"]}]}],"canonical_facts":{"dc:contributor":["Kindratenko, Volodymyr"],"dc:creator":["Kwark, Dou Hoon"],"dc:date":["2025-04-17","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Dou Hoon Kwark, accepted the attached license on 2025-04-17 at 12:29.","The student, Dou Hoon Kwark, submitted this Thesis for approval on 2025-04-17 at 12:30.","This Thesis was approved for publication on 2025-04-17 at 13:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21822 on 2025-10-19 at 18:09:31","Deep learning has catalyzed transformative breakthroughs in computer vision and related fields, but these advances often rely on large-scale datasets that are neither readily accessible nor cost-effective in many real-world contexts. In many practical domains—such as medical imaging and geological mapping—collecting large-scale, expertly annotated datasets is prohibitively expensive. This thesis investigates a range of strategies designed to alleviate the pervasive challenge of data scarcity. Through comprehensive studies on both discriminative and generative tasks—including segmentation, super-resolution, modality translation, and inpainting—we demonstrate novel frameworks that preserve strong predictive performance despite limited training data. Our central goal is to show that deep learning under constrained data can still deliver robust results, provided the modeling pipelines are carefully adapted to the problem at hand. First, we propose a hierarchical diffusion-based approach that synthesizes pseudo-healthy medical images with enhanced 3D consistency but moderate computational overhead. Second, we present a fusion strategy that integrates multiple 2D diffusion models into a lightweight 3D representation, improving volumetric realism when data points are limited. Finally, we explore a multi-encoder pipeline that leverages color-space transformations to better segment complex maps, demonstrating its utility in settings like geological digitization. Taken together, these contributions illustrate that addressing data scarcity does not require sacrificing performance. Rather, it calls for more nuanced model design—incorporating domain-specific insights, ensemble architectures, and complementary data transformations. 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