108 citations · 587 across the 36 of their papers we have counts for
6 papers · 1 filter
Addressing Failure Prediction by Learning Model Confidence
Charles Corbière, Nicolas Thome, Avner Bar-Hen +2
Assessing reliably the confidence of a deep neural network and predicting its failures is of primary importance for the practical deployment of these models. In this paper, we prop…
Multitask Classification and Segmentation for Cancer Diagnosis in Mammography
Thi-Lam-Thuy Le, Nicolas Thome, Sylvain Bernard +2
Annotation cost is a bottleneck for collecting massive data in mammography, especially for training deep neural networks. In this paper, we study the use of heterogeneous levels of…
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
Vincent Le Guen, Nicolas Thome
This paper addresses the problem of time series forecasting for non-stationary signals and multiple future steps prediction. To handle this challenging task, we introduce DILATE (D…
DualDis: Dual-Branch Disentangling with Adversarial Learning
Thomas Robert, Nicolas Thome, Matthieu Cord
In computer vision, disentangling techniques aim at improving latent representations of images by modeling factors of variation. In this paper, we propose DualDis, a new auto-encod…
MUREL: Multimodal Relational Reasoning for Visual Question Answering
Remi Cadene, Hedi Ben-younes, Matthieu Cord +1
Multimodal attentional networks are currently state-of-the-art models for Visual Question Answering (VQA) tasks involving real images. Although attention allows to focus on the vis…
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection
Hedi Ben-younes, Rémi Cadene, Nicolas Thome +1
Multimodal representation learning is gaining more and more interest within the deep learning community. While bilinear models provide an interesting framework to find subtle combi…