Tailored Deep Learning for Enhanced Performance in Resource-Constrained Settings
Year: 2024
Type: dissertation
Abstract: Tailored deep learning methods enable consistent and critical progress toward enhancing reasoning capabilities, particularly in data- and resource-constrained settings like healthcare. Improvements in knowledge distillation using information maximization enables cheaper optimization and domain-transfer for large vision models. Information-efficient contrastive learning for aligning images with textual data leads to better performance on small datasets. Structure-preserving generative adversarial networks help minimize visual variations in medical images. Conditional diffusion can be used to develop end-to-end models for synthesizing inherently-annotated histology tissue samples with pixel-perfect nuclei localization.
Source: Libra
DOI: 10.18130/yfg9-qx29
Author: Aman Shrivastava
Institution: University of Virginia
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6963093940
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