[PDF] Top 20 Rethinking deep active learning: Using unlabeled data at model training

... of using both labeled and unlabeled data during model training in deep active learning for image ...accurate model while requiring less labeled data, ... Voir le document complet

... detection, deep neural networks, knowledge ...in deep neural networks, historical handwritten documents analysis is still a challenging problem because of the requirement of large annotated training ... Voir le document complet

... attention model described in this article has been created to generate saliency maps that capture hierarchical and spatial features of chessboard, in order to predict the probability fixation for individual pixels ... Voir le document complet

... synthetic data is still useful for training strong face recognition ...the training set, therefore the CNN does not learn to rely on artefacts as discriminative features coding for identity, ... Voir le document complet

... the training cost of accurate surrogates by machine learning can rapidly increase with the number of ...this training becomes especially challenging as design regions grow larger than the optical ... Voir le document complet

... Sommaire Des progrès considérables ont été réalisés en robotique mobile au cours des dernières décennies et ces robots sont maintenant capables d’effectuer des tâches qu’on croyait au- paravant impossibles. Un facteur ... Voir le document complet

... new model, called ZiMM (Zero-inflated Mixture of Multinomial distributions) in order to capture long-term and blurry ...end-to-end deep-learning architecture called ZiMM Encoder-Decoder (ZiMM ED) ... Voir le document complet

... between training, innovation is analyzed using the panel data sets of CEREQ, INPI and the Ministry of ...a model that explore the relationship between innovation and train- ing, according to ... Voir le document complet

... machine learning, a branch of artifi- cial intelligence, to solve pattern recognition ...of deep learn- ing carried by the so-called deep neural networks algorithms that have become the state of the ... Voir le document complet

... Thanks to the National Museum of Costa Rica for their help with the collection, identification, and digitization of samples in the Costa Rican leaf-scan dataset. Special thanks to the Costa Rica Institute of Technology ... Voir le document complet

... that using data augmentation is an effective way to increase ...network model that has already been trained for a given task, and make it perform a second similar ...pre-trained Deep ... Voir le document complet

... required using deep learning ...mainly at the level of the dermis- epidermis junction that the differences can be visible ...made using the RCM images. Several deep ... Voir le document complet

... To understand the limits of how much could be gained through oracles, we have considered an active learning model in which the learner chooses the information rece[r] ... Voir le document complet

... spaces, deep reinforcement learning algorithms have attracted much interest in the robotics com- ...reinforcement learning implementation, reward signals have to be informative in the sense they have ... Voir le document complet

... (the learning process must be repeated for each new domain) and training-intensive (good performance requires a relatively large number of train- ing documents in the given domain, with manually assigned ... Voir le document complet

... 2: Model accuracy evolution through 100 epochs ...feature at a time, run the train- ing process and compute the accuracy of the ...the model by running it with all the ... Voir le document complet

... Corresponding author: kamal.mohy el dine@sigma-clermont.fr Abstract. Robots executing force controlled tasks require accurate per- ception of the applied force in order to guarantee precision. However, dynamic motions ... Voir le document complet

... Abstract Learning from uncertain data has been drawing increasing attention in recent ...uncertain data, but also furthermore reduce epistemic uncertainty by querying the most valuable uncertain ... Voir le document complet

... Buszkowski has proposed in [1] an algorithm, called RG, that learns rigid clas- sical categorial grammars from functor-argument structures in Gold’s model. A grammar is said rigid if every word in its lexicon is ... Voir le document complet

... Benslimane et Al. 8 research provides two contributions: (1) it proposes an architecture to create a database (of images) of logistic vehicles. And (2) it uses this base for the training of classification models ... Voir le document complet