Pedro Antonio Hernandez Zamarron
Data Selection via Semantic Similarity for Resource-Efficient Transfer Learning of Image Classifiers.
Rel. Andrea Calimera, Valentino Peluso. Politecnico di Torino, Master of science program in Data Science And Engineering, 2022
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Abstract
The generation of data such as text, audio and video has rapidly increased over the past 20 years through technological advancements such as IoT (internet of things) and social networks. This data contains information that can help make educated decisions by providing helpful insights. For example, social media data can provide ads tailored for users, leading to more effective advertising. On the other hand, data generated by sensors in IoT devices can tell manufacturers how to enhance the performance of their products. Hence, there is much interest in extracting the value present in data. However, said data is often unstructured and does not convey meaningful information.
Consequently, many data analysis methods have arisen
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