Deep active learning using Monte Carlo Dropout

• Autor(a) principal: Moura, Lucas Albuquerque Medeiros de
• Data de Publicação: 2018
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
• Texto Completo: http://www.teses.usp.br/teses/disponiveis/45/45134/tde-17032019-222659/
• topic: Active learning; Análise de sentimento; Aprendizado ativo; Aprendizado profundo; Deep learning; Sentiment analysis

Deep Learning models rely on a huge amount of labeled data to be created. However, there are a number of areas where labeling data is a costly process, making Deep Learning approaches unfeasible. One way to handle that situation is by using the Active Learning technique. Initially, it creates a model with the available labeled data. After that, it incrementally chooses new unlabeled data that will potentially increase the model accuracy, if added to the training data. To select which data will be labeled next, this technique requires a measurement of uncertainty from the model prediction, which is usually not computed for Deep Learning methods. A new approach has been proposed to measure uncertainty in those models, called Monte Carlo Dropout . This technique allowed Active Learning to be used together with Deep Learning for image classification. This research will evaluate if modeling uncertainty on Deep Learning models with Monte Carlo Dropout will make the use of Active Learning feasible for the task of sentiment analysis, an area with huge amount of data, but few of them labeled.