Deep Active Learning Explored Across Diverse Label Spaces

abstract: Deep learning architectures have been widely explored in computer vision and have depicted commendable performance in a variety of applications. A fundamental challenge in training deep networks is the requirement of large amounts of labeled training data. While gathering large quantities of unlabeled data is cheap and easy, annotating the data is an expensive process in terms of time, labor and human expertise. Thus, developing algorithms that minimize the human effort in training deep models is of immense practical importance. Active learning algorithms automatically identify salient and exemplar samples from large amounts of unlabeled data and can augment maximal information to supervised learning models, thereby reducing the human annotation effort in training machine learning models. The goal of this dissertation is to fuse ideas from deep learning and active learning and design novel deep active learning algorithms. The proposed learning methodologies explore diverse label spaces to solve different computer vision applications. Three major contributions have emerged from this work; (i) a deep active framework for multi-class image classication, (ii) a deep active model with and without label correlation for multi-label image classi- cation and (iii) a deep active paradigm for regression. Extensive empirical studies on a variety of multi-class, multi-label and regression vision datasets corroborate the potential of the proposed methods for real-world applications. Additional contributions include: (i) a multimodal emotion database consisting of recordings of facial expressions, body gestures, vocal expressions and physiological signals of actors enacting various emotions, (ii) four multimodal deep belief network models and (iii) an in-depth analysis of the effect of transfer of multimodal emotion features between source and target networks on classification accuracy and training time. These related contributions help comprehend the challenges involved in training deep learning models and motivate the main goal of this dissertation.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Diseño de un modelo para la clasificación de dolor postquirúrgico en pacientes pediátricos no-comunicativos del Hospital Universitario San Vicente Fundación - Medellín

RESUMEN: La evaluación del nivel de dolor postoperatorio (POP) percibido en los pacientes ha ido mejorando en los ámbitos hospitalarios, es tanto así, que se han desarrollado protocolos de control del dolor y escalas de evaluación del mismo. Sin embargo, la evaluación del dolor en la población infantil presenta dificultades en torno a la incapacidad de ellos para describir verbalmente la ubicación, duración e intensidad de la experiencia dolorosa. A pesar de haber escalas de evaluación de dolor dirigidas especialmente a los niños no-comunicativos, existen falencias en cuanto a la subjetividad del personal quien evalúa el dolor