Essays about: "zero-shot learning"
Showing result 1 - 5 of 25 essays containing the words zero-shot learning.
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1. Bridging Language & Data : Optimizing Text-to-SQL Generation in Large Language Models
University essay from Linköpings universitet/Artificiell intelligens och integrerade datorsystemAbstract : This thesis explores text-to-SQL generation using Large Language Models within a financial context, aiming to assess the efficacy of current benchmarks and techniques. The central investigation revolves around the accuracy of the BIRD-Bench benchmark and the applicability of text-to-SQL models in real-world scenarios. READ MORE
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2. Topological regularization and relative latent representations
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : This Master's Thesis delves into the application of topological regularization techniques and relative latent representations within the realm of zero-shot model stitching. Building upon the prior work of Moschella et al. READ MORE
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3. Anomaly Detection with Machine Learning using CLIP in a Video Surveillance Context
University essay from Linköpings universitet/DatorseendeAbstract : This thesis explores the application of Contrastive Language-Image Pre-Training (CLIP), a vision-language model, in an automated video surveillance system for anomaly detection. The ability of CLIP to perform zero-shot learning, coupled with its robustness against minor image alterations due to its lack of reliance on pixel-level image analysis, makes it a suitable candidate for this application. READ MORE
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4. Generation of Synthetic Traffic Sign Images using Diffusion Models
University essay from Linköpings universitet/DatorseendeAbstract : In the area of Traffic Sign Recognition (TSR), deep learning models are trained to detect and classify images of traffic signs. The amount of data available to train these models is often limited, and collecting more data is time-consuming and expensive. READ MORE
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5. Prompt-learning and Zero-shot Text Classification with Domain-specific Textual Data
University essay from Uppsala universitet/Institutionen för lingvistik och filologiAbstract : The rapid growth of textual data in the digital age presents unique challenges in domain-specific text classification, particularly the scarcity of labeled data for many applications, due to expensive cost of manual labeling work. In this thesis, we explore the applicability of prompt-learning method, which is well-known for being suitable in few-shot scenarios and much less data-consuming, as an emerging alternative to traditional fine-tuning methods, for domain-specific text classification in the context of customer-agent interactions in the retail sector. READ MORE
