4 papers
Few-Shot Few-Shot Learning and the role of Spatial Attention
Yann Lifchitz, Yannis Avrithis, Sylvaine Picard
Few-shot learning is often motivated by the ability of humans to learn new tasks from few examples. However, standard few-shot classification benchmarks assume that the representat…
n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True Error
Michel Moukari, Loïc Simon, Sylvaine Picard +1
As deep learning applications are becoming more and more pervasive in robotics, the question of evaluating the reliability of inferences becomes a central question in the robotics…
Dense Classification and Implanting for Few-Shot Learning
Yann Lifchitz, Yannis Avrithis, Sylvaine Picard +1
Training deep neural networks from few examples is a highly challenging and key problem for many computer vision tasks. In this context, we are targeting knowledge transfer from a…
FUNN: Flexible Unsupervised Neural Network
David Vigouroux, Sylvain Picard
Deep neural networks have demonstrated high accuracy in image classification tasks. However, they were shown to be weak against adversarial examples: a small perturbation in the im…