Recognizing variable stars is a task of interest in the astronomy community. Currently, this task has taken advantage of deep learning algorithms. However, these algorithms require a large amount of data to achieve high levels of precision. In this work, self-supervised learning is proposed to improve the classification of variable stars considering a reduced amount of data using recurrent networks. The experiments in Gaia dataset show that the proposed approach allows to improve performance, when compared with traditional initialization schemes, up to 7% and 13% in real databases in semi-supervised learning scenarios. In future work, we propose considering experiments with other variable star databases.

During the last few years, the field of dynamical systems has been developing innovative tools to study the asymptotic behavior of different optimizers in the context of neural networks. In this work, we redefine an extensively studied optimizer, employing classical techniques from hyperbolic geometry. This new definition is linked to a non-linear differential equation as a continuous limit. Additionally, by utilizing Lyapunov stability concepts, we analyze the asymptotic behavior of its critical points.

Dictionary-based and part-based methods are among the most popular approaches to visual recognition. In both methods, a mid-level representation is built on top of low-level image descriptors and high-level classifiers are trained on top of the mid-level representation. While earlier methods built the mid-level representation without supervision, there is currently great interest in learning both representations jointly to make the mid-level representation more discriminative. In this work we propose a new approach to visual recognition that jointly learns a shared, discriminative, and compact mid-level representation and a compact high-level representation. By using a structured output learning framework, our approach directly handles the multiclass case at both levels of abstraction. Moreover, by using a group-sparse prior in the structured output learning framework, our approach encourages sharing of visual words and thus reduces the number of words used to represent each class. We test our proposed method on several popular benchmarks. Our results show that, by jointly learning mid- and high-level representations, and fostering the sharing of discriminative visual words among target classes, we are able to achieve state-of-the-art recognition performance using far less visual words than previous approaches.

Currently, Bag-of-Visual-Words (BoVW) and part-based methods are the most popular approaches for visual recognition. In both cases, a mid-level representation is built on top of low-level image descriptors and top-level classifiers use this mid-level representation to achieve visual recognition. While in current part-based approaches, mid- and top-level representations are usually jointly trained, this is not the usual case for BoVW schemes. A main reason for this is the complex data association problem related to the usual large dictionary size needed by BoVW approaches. As a further observation, typical solutions based on BoVW and part-based representations are usually limited to extensions of binary classification schemes, a strategy that ignores relevant correlations among classes. In this work we propose a novel hierarchical approach to visual recognition based on a BoVW scheme that jointly learns suitable mid- and top-level representations. Furthermore, using a max-margin learning framework, the proposed approach directly handles the multiclass case at both levels of abstraction. We test our proposed method using several popular benchmark datasets. As our main result, we demonstrate that, by coupling learning of mid- and top-level representations, the proposed approach fosters sharing of discriminative visual words among target classes, being able to achieve state-of-the-art recognition performance using far less visual words than previous approaches.