Merging chrominance and luminance in early, medium, and late fusion using Convolutional Neural Networks

The field of Machine Learning has received extensive attention in recent years. More particularly, computer vision problems have got abundant consideration as the use of images and pictures in our daily routines is growing. The classification of images is one of the most important tasks that can be used to organize, store, retrieve, and explain pictures. In order to do that, researchers have been designing algorithms that automatically detect objects in images. During last decades, the common approach has been to create sets of features -- manually designed -- that could be exploited by image classification algorithms. More recently, researchers designed algorithms that automatically learn these sets of features, surpassing state-of-the-art performances. However, learning optimal sets of features is computationally expensive and it can be relaxed by adding prior knowledge about the task, improving and accelerating the learning phase. Furthermore, with problems with a large feature space the complexity of the models need to be reduced to make it computationally tractable (e.g. the recognition of human actions in videos). Consequently, we propose to use multimodal learning techniques to reduce the complexity of the learning phase in Artificial Neural Networks by incorporating prior knowledge about the connectivity of the network. Furthermore, we analyze state-of-the-art models for image classification and propose new architectures that can learn a locally optimal set of features in an easier and faster manner. In this thesis, we demonstrate that merging the luminance and the chrominance part of the images using multimodal learning techniques can improve the acquisition of good visual set of features. We compare the validation accuracy of several models and we demonstrate that our approach outperforms the basic model with statistically significant results

Beyond temperature scaling:Obtaining well-calibrated multiclass probabilities with Dirichlet calibration

Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence. With neural networks, calibration can be improved by temperature scaling, a method to learn a single corrective multiplicative factor for inputs to the last softmax layer. On non-neural models the existing methods apply binary calibration in a pairwise or one-vs-rest fashion. We propose a natively multiclass calibration method applicable to classifiers from any model class, derived from Dirichlet distributions and generalising the beta calibration method from binary classification. It is easily implemented with neural nets since it is equivalent to log-transforming the uncalibrated probabilities, followed by one linear layer and softmax. Experiments demonstrate improved probabilistic predictions according to multiple measures (confidence-ECE, classwise-ECE, log-loss, Brier score) across a wide range of datasets and classifiers. Parameters of the learned Dirichlet calibration map provide insights to the biases in the uncalibrated model.Comment: Accepted for presentation at NeurIPS 201

Classifier Calibration: A survey on how to assess and improve predicted class probabilities

This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration. A well-calibrated classifier correctly quantifies the level of uncertainty or confidence associated with its instance-wise predictions. This is essential for critical applications, optimal decision making, cost-sensitive classification, and for some types of context change. Calibration research has a rich history which predates the birth of machine learning as an academic field by decades. However, a recent increase in the interest on calibration has led to new methods and the extension from binary to the multiclass setting. The space of options and issues to consider is large, and navigating it requires the right set of concepts and tools. We provide both introductory material and up-to-date technical details of the main concepts and methods, including proper scoring rules and other evaluation metrics, visualisation approaches, a comprehensive account of post-hoc calibration methods for binary and multiclass classification, and several advanced topics