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 20 for “"zero-shot learning"”.
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Generalization and Specialization in Zero-Shot Learning
… has made remarkable success due to deep learning techniques and vast amounts of labeled data. However, in real-world scenarios, the data distribution is long-tailed, making acquiring sufficient labels difficult, thus hindering the performance of deep models. To overcome these obstacles, …
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Optimising image feature zero-shot-learning with EEG
… not present during training, i.e. we apply zero-shot learning.<br/><br/>To achieve this goal, we first investigate a discriminative feature extraction process for EEG brain data and establish a baseline evaluation of exemplar and category-level decoding. Once competitive decoding rates are …
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Zero-shot learning to execute tasks with robots
… the art in robotic planning with reinforcement learning. We are interested in designing a generalizable framework with several features, namely: allowing for zero-shot learning agents that are robust and resilient in the event of failing midway during a task, allowing us to detect failures, and …
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Class Representative Projection for Text-based Zero-Shot Learning
… been significant advances in supervised machine learning and enormous benefits from deep learning for a range of diverse applications. Despite the success of deep learning, in reality, very few works have shown progress in text classification. Transfer learning, known as the zero-shot learning …
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Few-Shot and Zero-Shot Learning for Information Extraction
… 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 learning and …
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Mid-level representations for action recognition and zero-shot learning
… and the other two problems in the task of zero-shot learning. For the first problem, we devise a representation suitable for characterising human actions on the basis of a sequence of pose estimates generated by an RGB-D sensor. We show that discriminate sequence of poses typically occur …
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Deep Attentional Modulation for Zero-shot Learning in Object Recognition
… to recognize objects from just a description (zero-shot) or a few examples (few-shot). Traditionally, artificial neural networks have struggled at reproducing this ability, with large performance drops in the zero-and few-shot domains caused by overfitting. Most methods are focusing on learning …
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Generative Models for Domain-Specific Summarization
… and GPT-3.5-Turbo were analyzed in both their zero-shot learning and fine-tuned performance against state-of-the-art models. In zero-shot learning, generative models were superior in most cases to the state-of-the- art models, whereas the fine-tuned models could learn with less information …
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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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VISUAL AND SEMANTIC KNOWLEDGE TRANSFER FOR NOVEL TASKS
… is a critical component in a supervised machine learning system. Many successful applications of learning systems on various tasks are based on a large amount of labeled data. For example, deep convolutional neural networks have surpassed human performance on ImageNet classification, which …
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SAMPLS: A prompt engineering approach using Segment-Anything-Model for PLant Science research
… the Segment Anything Model (SAM) to evaluate its zero-shot learning capability and whether prompt engineering can reduce the effort and time consumed in dataset annotation, facilitating a semi-automated training process. Our proposed method improved the detection rate of cells and reduced the …
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Innovating the Study of Self-Regulated Learning: An Exploration through NLP, Generative AI, and LLMs
… models (LLMs) to analyze student self-regulated learning (SRL) strategies in response to exam wrappers. Exam wrappers are structured reflection activities that prompt students to practice SRL after they get their graded exams back. The dissertation consists of three manuscripts that compare …
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Grammatical analysis of Maltese text
… experimented with cross-lingual transfer and zero-shot learning. These help in understanding to what extent grammatical information can be generalised to little or unseen, but related languages. The final results compare the various models and language settings to analyse which of these can …
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Conversation understanding and realistic artificial crash data generation with deep learning
… and realistic crash data generation with deep learning. Conversation understanding includes conversational outcome, formality, and politeness prediction. For conversation outcome prediction, we use recorded audio calls collected from a partnering Fortune 500 firm that captures conversations …
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User-centered intrusion detection using heterogeneous data
… to automate intrusion detection via machine learning solutions. This generally comes with numerous challenges, among others high class imbalance, changing target concepts and difficulties to conduct sound evaluation. In this thesis, we adopt a user-centered anomaly detection perspective to …
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Cold-start universal information extraction
… type, and (3) design features and train machine learning models to extract knowledge elements. In practice, this process is very expensive as each step involves extensive human effort which is not always available, for example, to specify the knowledge types for a particular scenario, both …
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Advanced Computer Vision for Smart Retail: From Anomaly Detection to Fine-grained Product Classification
… retail environments. Second, we propose a robust zero-shot fine-grained product classification pipeline leveraging advanced vision models, specifically CLIP and DINOv2. By utilizing visual embeddings and prototype-based classification strategies, our approach effectively distinguishes visually …
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End-to-end Contextual Speech Recognition and Understanding
… to a pipeline system. SPB further enabled zero-shot learning of unseen slot types by providing a list of possible named entities for that slot. Furthermore, a knowledge-aware audio-grounded (KA2G) generative SLU framework is introduced which performs slot filling by prompt and response in …
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Machine-learning-enabled optimization and online monitoring for efficient and high-quality smart drying
… this dissertation develops a suite of machine-learning-based process control tools to enable smart drying with improved process efficiency and product quality. The contributions of this dissertation are summarized as follows. It is important to devise a drying strategy to optimize drying …