Search Results for author: Nicolas Thome

Found 35 papers, 22 papers with code

Deep Time Series Forecasting with Shape and Temporal Criteria

1 code implementation9 Apr 2021 Vincent Le Guen, Nicolas Thome

This paper addresses the problem of multi-step time series forecasting for non-stationary signals that can present sudden changes.

Dynamic Time Warping Time Series +1

U-Net Transformer: Self and Cross Attention for Medical Image Segmentation

1 code implementation10 Mar 2021 Olivier Petit, Nicolas Thome, Clément Rambour, Luc Soler

Medical image segmentation remains particularly challenging for complex and low-contrast anatomical structures.

Medical Image Segmentation

Confidence Estimation via Auxiliary Models

no code implementations11 Dec 2020 Charles Corbière, Nicolas Thome, Antoine Saporta, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez

In this paper, we introduce a novel target criterion for model confidence, namely the true class probability (TCP).

Domain Adaptation Image Classification +1

Probabilistic Time Series Forecasting with Shape and Temporal Diversity

1 code implementation NeurIPS 2020 Vincent Le Guen, Nicolas Thome

We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while being sharp and accurate.

Point Processes Probabilistic Time Series Forecasting +1

Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity

1 code implementation14 Oct 2020 Vincent Le Guen, Nicolas Thome

We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while being sharp and accurate.

Point Processes Probabilistic Time Series Forecasting +1

Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

no code implementations ICLR 2021 Vincent Le Guen, Yuan Yin, Jérémie Dona, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Thome, Patrick Gallinari

Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields.

Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction

1 code implementation CVPR 2020 Vincent Le Guen, Nicolas Thome

Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video prediction methods.

Video Prediction

Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models

2 code implementations NeurIPS 2019 Vincent Le Guen, Nicolas Thome

We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization.

Dynamic Time Warping Time Series +2

DualDis: Dual-Branch Disentangling with Adversarial Learning

1 code implementation3 Jun 2019 Thomas Robert, Nicolas Thome, Matthieu Cord

To effectively separate the information, we propose to use a combination of regular and adversarial classifiers to guide the two branches in specializing for class and attribute information respectively.

Data Augmentation Image Manipulation +1

BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection

1 code implementation31 Jan 2019 Hedi Ben-Younes, Rémi Cadene, Nicolas Thome, Matthieu Cord

We demonstrate the practical interest of our fusion model by using BLOCK for two challenging tasks: Visual Question Answering (VQA) and Visual Relationship Detection (VRD), where we design end-to-end learnable architectures for representing relevant interactions between modalities.

Question Answering Representation Learning +3

HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning

no code implementations ECCV 2018 Thomas Robert, Nicolas Thome, Matthieu Cord

In this paper, we introduce a new model for leveraging unlabeled data to improve generalization performances of image classifiers: a two-branch encoder-decoder architecture called HybridNet.

Classification General Classification +1

Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings

1 code implementation30 Apr 2018 Micael Carvalho, Rémi Cadène, David Picard, Laure Soulier, Nicolas Thome, Matthieu Cord

Designing powerful tools that support cooking activities has rapidly gained popularity due to the massive amounts of available data, as well as recent advances in machine learning that are capable of analyzing them.

Cross-Modal Retrieval

Deformable Part-based Fully Convolutional Network for Object Detection

no code implementations19 Jul 2017 Taylor Mordan, Nicolas Thome, Matthieu Cord, Gilles Henaff

Existing region-based object detectors are limited to regions with fixed box geometry to represent objects, even if those are highly non-rectangular.

Object Detection

Gossip training for deep learning

1 code implementation29 Nov 2016 Michael Blot, David Picard, Matthieu Cord, Nicolas Thome

We address the issue of speeding up the training of convolutional networks.

Maxmin convolutional neural networks for image classification

no code implementations25 Oct 2016 Michael Blot, Matthieu Cord, Nicolas Thome

Convolutional neural networks (CNN) are widely used in computer vision, especially in image classification.

Classification General Classification +1

Master's Thesis : Deep Learning for Visual Recognition

1 code implementation18 Oct 2016 Rémi Cadène, Nicolas Thome, Matthieu Cord

Our last contribution is a framework, build on top of Torch7, for training and testing deep models on any visual recognition tasks and on datasets of any scale.

WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks

1 code implementation CVPR 2016 Thibaut Durand, Nicolas Thome, Matthieu Cord

In this paper, we introduce a novel framework for WEakly supervised Learning of Deep cOnvolutional neural Networks (WELDON).

Multiple Instance Learning

Deep Neural Networks Under Stress

1 code implementation11 May 2016 Micael Carvalho, Matthieu Cord, Sandra Avila, Nicolas Thome, Eduardo Valle

In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many datasets.

Transfer Learning

Fantope Regularization in Metric Learning

no code implementations CVPR 2014 Marc T. Law, Nicolas Thome, Matthieu Cord

This paper introduces a regularization method to explicitly control the rank of a learned symmetric positive semidefinite distance matrix in distance metric learning.

Face Verification General Classification +2

Top-Down Regularization of Deep Belief Networks

no code implementations NeurIPS 2013 Hanlin Goh, Nicolas Thome, Matthieu Cord, Joo-Hwee Lim

We suggest a deep learning strategy that bridges the gap between the two phases, resulting in a three-phase learning procedure.

Global Optimization Object Recognition

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