  Current deep learning solutions are well known for not informing whether they
can reliably classify an example during inference. One of the most effective
ways to build more reliable deep learning solutions is to improve their
performance in the so-called out-of-distribution detection task, which
essentially consists of "know that you do not know" or "know the unknown". In
other words, out-of-distribution detection capable systems may reject
performing a nonsense classification when submitted to instances of classes on
which the neural network was not trained. This thesis tackles the defiant
out-of-distribution detection task by proposing novel loss functions and
detection scores. Uncertainty estimation is also a crucial auxiliary task in
building more robust deep learning systems. Therefore, we also deal with this
robustness-related task, which evaluates how realistic the probabilities
presented by the deep neural network are. To demonstrate the effectiveness of
our approach, in addition to a substantial set of experiments, which includes
state-of-the-art results, we use arguments based on the principle of maximum
entropy to establish the theoretical foundation of the proposed approaches.
Unlike most current methods, our losses and scores are seamless and principled
solutions that produce accurate predictions in addition to fast and efficient
inference. Moreover, our approaches can be incorporated into current and future
projects simply by replacing the loss used to train the deep neural network and
computing a rapid score for detection.
