Gustavo Carneiro

TL;DR: This work proposes a unsupervised framework to learn a deep convolutional neural network for single view depth prediction, without requiring a pre-training stage or annotated ground-truth depths, and shows that this network trained on less than half of the KITTI dataset gives comparable performance to that of the state-of-the-art supervised methods for singleView depth estimation.

TL;DR: The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost and to be fairly robust to parameter tuning.

TL;DR: In this paper, an unsupervised framework was proposed to learn a deep CNN for single view depth prediction without requiring a pre-training stage or annotated ground truth depths, by training the network in a manner analogous to an autoencoder.

TL;DR: A new methodology that combines deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance (MR) data is introduced, producing a methodology that needs small training sets and produces accurate segmentation results.

TL;DR: In this article, triplet networks are used for local image descriptor learning and a global loss is proposed to minimize the overall classification error in the training set, which can improve the generalization capability of the model.