Search Results for author: Sylvaine Picard

Found 9 papers, 0 papers with code

Dataset Definition Standard (DDS)

no code implementations7 Jan 2021 Cyril Cappi, Camille Chapdelaine, Laurent Gardes, Eric Jenn, Baptiste Lefevre, Sylvaine Picard, Thomas Soumarmon

This document gives a set of recommendations to build and manipulate the datasets used to develop and/or validate machine learning models such as deep neural networks.

Local Propagation for Few-Shot Learning

no code implementations5 Jan 2021 Yann Lifchitz, Yannis Avrithis, Sylvaine Picard

The challenge in few-shot learning is that available data is not enough to capture the underlying distribution.

Few-Shot Learning

Ensuring Dataset Quality for Machine Learning Certification

no code implementations3 Nov 2020 Sylvaine Picard, Camille Chapdelaine, Cyril Cappi, Laurent Gardes, Eric Jenn, Baptiste Lefèvre, Thomas Soumarmon

In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems.

Few-Shot Few-Shot Learning and the role of Spatial Attention

no code implementations18 Feb 2020 Yann Lifchitz, Yannis Avrithis, Sylvaine Picard

This motivates us to study a problem where the representation is obtained from a classifier pre-trained on a large-scale dataset of a different domain, assuming no access to its training process, while the base class data are limited to few examples per class and their role is to adapt the representation to the domain at hand rather than learn from scratch.

Few-Shot Learning

n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True Error

no code implementations20 Aug 2019 Michel Moukari, Loïc Simon, Sylvaine Picard, Frédéric Jurie

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 community.

Monocular Depth Estimation


no code implementations27 Sep 2018 Michel Moukari, Loïc Simon, Sylvaine Picard, Frédéric Jurie

One contribution of this article is to draw attention on existing metrics developed in the forecast community, designed to evaluate both the sharpness and the calibration of predictive uncertainty.

Monocular Depth Estimation

Deep multi-scale architectures for monocular depth estimation

no code implementations8 Jun 2018 Michel Moukari, Sylvaine Picard, Loic Simon, Frédéric Jurie

This paper aims at understanding the role of multi-scale information in the estimation of depth from monocular images.

Monocular Depth Estimation

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