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.
Results
Showing 1 to 8 of 8 for “"Residual learning"”.
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Improving Extreme Low-light Image Denoising via Residual Learning
… extremely low light conditions. Recently, deep learning based approaches have been presented that have higher objective quality than traditional methods, but they usually have high computation cost which makes them impractical to use in real-time applications or where the computational resource …
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Physics-based and data-driven inversion of magnetotelluric data for subsurface imaging
… ensemble-based conditioning with physics-guided residual learning. The approach combines an ensemble-approximated conditional Gaussian process (EnsCGP), which produces physically consistent resistivity estimates and ensemble-based uncertainty within the span of a prior model space, with a …
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Vision task driven image super-resolution and image enhancement
… use cases. Recent advancements in deep learning-based methods may contribute towards the enhancement of low-light images to high-quality images with enough exposure. However, these pixel domain signal recovery metrics may not directly correlate to the machine vision tasks like key points …
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Research on 3D reconstruction based on 2D face images.
… the neural network model by using the idea of residual learning to train the network model incrementally, emphasizing the reconstruction of the model for deep information. Face data characteristics are first extracted using the encoding and decoding layers, and then face features are learned …
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Online Machine Learning for Wireless Communications: Channel Estimation, Receive Processing, and Resource Allocation
Machine learning (ML) has shown its success in many areas such as computer vision, natural language processing, robot control, and gaming. ML also draws significant attention in the wireless communication society. However, applying ML schemes to wireless communication networks is not …
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Neural recommender models for sparse and skewed behavioral data
… others are not. We develop a self-supervised learning framework where the aggregate co-occurrences guide the recommendation problem while providing room to learn these variations among the item associations. As a result, we improve coverage to ~100% (up from 5%) of the inventory and increase …
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Machine Learning Methods for Brain Image Analysis
… connectivity in the brain. I propose to use deep learning algorithms for the 2D segmentation of EM images. I designed an automated pipeline with novel insights that was able to achieve state-of-the-art performance on the segmentation of the \textit{Drosophila} brain. I also propose a novel …
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Applications of Deep Learning, Machine Learning, and Remote Sensing to Improving Air Quality and Solar Energy Production
… model into a RF-CNN joint model that adopts a residual learning ideology that forces the CNN part to most effectively exploit the information in satellite images that is only “orthogonal” to meteorology. The RF-CNN joint model achieved low normalized root mean square error for PM2.5 of within …