|
|
|
|
|
Yuqing Hu, Stéphane Pateux and Vincent Gripon
In many real-life problems, it is difficult to acquire or label large amounts of data, resulting in so-called few-shot learning problems. However, few-shot classification is a challenging problem due to the uncertainty caused by using few labeled samples...
ver más
|
|
|
|
|
|
|
|
|
|
|
Carlos Lassance, Vincent Gripon and Antonio Ortega
Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art performance in many machine learning tasks. The core principle of DL methods consists of training composite architectures in an end-to-end fashion, where inputs...
ver más
|
|
|
|
|
|
|
|
|
|
|
Myriam Bontonou, Louis Béthune and Vincent Gripon
In the context of few-shot learning, one cannot measure the generalization ability of a trained classifier using validation sets, due to the small number of labeled samples. In this paper, we are interested in finding alternatives to answer the question:...
ver más
|
|
|
|
|
|
|
|
|
|
|
Guillaume Coiffier, Ghouthi Boukli Hacene and Vincent Gripon
Deep Neural Networks are state-of-the-art in a large number of challenges in machine learning. However, to reach the best performance they require a huge pool of parameters. Indeed, typical deep convolutional architectures present an increasing number of...
ver más
|
|
|
|
|
|
|
|
|
|
|
Vincent Gripon, Matthias Löwe and Franck Vermet
Nearest neighbor search is a very active field in machine learning. It appears in many application cases, including classification and object retrieval. In its naive implementation, the complexity of the search is linear in the product of the dimension a...
ver más
|
|
|
|
|
|