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 20 of 53 for “"few-shot learning"”.
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Few Shot Learning for Rare Disease Diagnosis
… rare disease patients. Recent advances in deep learning have considerably improved the accuracy of medical diagnosis. However, much of the success thus far is contingent on the availability of large annotated datasets containing thousands of examples per condition for training machine learning …
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Feature extractor stacking for cross-domain few-shot learning
Cross-domain few-shot learning (CDFSL) addresses learning problems where knowledge needs to be transferred from one or more source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSL methods generally construct a universal model that …
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Deep Zero- and Few-shot Learning in Computer Vision
… number of samples is not consistent with human's learning abilities, as humans have the ability to understand novel concepts from limited examples. Transfer learning is a machine learning topic that addresses the above limitations in current deep models, and it studies how to make machines exploit …
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Deep Zero- and Few-shot Learning in Computer Vision
… number of samples is not consistent with human's learning abilities, as humans have the ability to understand novel concepts from limited examples. Transfer learning is a machine learning topic that addresses the above limitations in current deep models, and it studies how to make machines exploit …
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A few-shot learning method for single-object visual anomaly detection
We propose a few-shot learning method for visually inspecting single objects in an industrial setting. The proposed method is able to identify whether or not an object is defective by comparing its visual appearance with a small set of images of the “working” object, i.e., the object that passes …
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Advances in Few-Shot Learning for Image Classification and Tabular Data
Modern machine learning systems frequently operate in dynamic, data-scarce environments where assembling large labelled datasets is infeasible or impractical. Key examples include personalisation, federated learning, and privacy-sensitive applications -- each requiring robust learning from minimal …
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Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning
Learning is the ability to generalise beyond training examples; but because many generalisations are consistent with a given set of observations, all machine learning methods rely on inductive biases to select certain generalisations over others. This thesis explores how the model structure and …
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A Patch-as-Filter Method for Same-Different Problems with Few-Shot Learning
… are still a huge undertaking, particularly in few-shot learning cases. Little is known, especially in solving the Same-Different (SD) task, which is a type of visual reasoning task that requires seeking pattern repetitions in a single image. In this thesis, we propose a patch-as-filter method …
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Few-Shot and Zero-Shot Learning for Information Extraction
… attribute-value extraction in e-commerce, with few labeled (few-shot learning) or even no labeled (zero-shot learning) training data. We explore multi-source auxiliary information and novel learning techniques to integrate semantic auxiliary information with the input text to improve few-shot …
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Learning with Limited Labeled Data: Techniques and Applications
… development and evaluation of advanced machine learning algorithms to solve the following research questions: (1) How to learn novel classes with limited labeled data, (2) How to adapt a large pre-trained model to the target domain if only unlabeled data is available, (3) How to boost the …
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Data-Efficient Machine Learning with Applications to Cardiology
Deep learning models have demonstrated impressive capabilities in many settings including computer vision, natural language generation, and speech processing. However, an important shortcoming of these models is that they often need to be trained on large datasets in order to be most effective. In …
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Upgrading the Protein Formulation Toolbox through Predictive and Explanatory Models
… surface methodology, conventional machine learning (ML), few shot learning, molecular dynamics, and graph theory are applied to protein formulation problems in both the solid and solution states. A broad overview of models developed in this thesis is presented in the figure below. [Figure …
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Neural Feature Fields for Language-Guided Robot Manipulation
… from 2D foundation models. We present a few-shot learning method for 6-DOF grasping and placing that harnesses these strong spatial and semantic priors to achieve in-the-wild generalization to unseen objects. Using features distilled from a vision-language model, CLIP, we present a way to …
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Uncertainty-Inclusive Contrastive Learning for Leveraging Synthetic Images
… in using synthesized training data to improve few-shot learning performance. Prevailing approaches treat all generated data as uniformly important, neglecting the fact that the quality of generated images varies across different domains, datasets, and methods of generation. Using poor-quality …
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ExoSpotter: Few Shot Relevance Feedback For Learning High Recall Exoplanet Search
… human input is desirable. Unfortunately, very few labeled training data are available (i.e., light curves labeled as planet candidates), which makes automatic classification di cult. Here, we propose a new approach to identify planet candidates using relevance-feedback accelerated few-shot …
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Implementation of a cross-platform automated Bayesian data modeling system
… in ClojureCat are several implementations of few-shot learning, in which for several real-world datasets, we utilize extremely sparse label sets and CrossCat's learned structure of the data to draw meaningful conclusions, make predictions, and further analyze the high-dimensional data.
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Software and Hardware Co-design for Efficient Neural Networks
… the cloud. Finally, I show how automated machine learning techniques can be improved with hardware-awareness to produce efficient network architectures for emerging types of neural networks and new learning problem setups. Hardware-aware network architecture search (NAS) is able to discover more …
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Improving few-shot object detection by saving and hallucinating examples
Learning to detect an object in an image from very few training examples - few-shot object detection - is challenging, because the classifier that sees proposal boxes has very little training data. A particularly challenging training regime occurs when there are one or two training examples. In …
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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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