1.4k citations · 2.6k across the 79 of their papers we have counts for
8 papers · 1 filter
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Tuan-Hung Vu, Himalaya Jain, Maxime Bucher +2
Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks,…
HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning
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…
SHADE: Information Based Regularization for Deep Learning
Michael Blot, Thomas Robert, Nicolas Thome +1
Regularization is a big issue for training deep neural networks. In this paper, we propose a new information-theory-based regularization scheme named SHADE for SHAnnon DEcay. The o…
SHADE: Information-Based Regularization for Deep Learning
Michael Blot, Thomas Robert, Nicolas Thome +1
Regularization is a big issue for training deep neural networks. In this paper, we propose a new information-theory-based regularization scheme named SHADE for SHAnnon DEcay. The o…
Images & Recipes: Retrieval in the cooking context
Micael Carvalho, Rémi Cadène, David Picard +2
Recent advances in the machine learning community allowed different use cases to emerge, as its association to domains like cooking which created the computational cuisine. In this…
Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings
Micael Carvalho, Rémi Cadène, David Picard +3
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…
Finding beans in burgers: Deep semantic-visual embedding with localization
Martin Engilberge, Louis Chevallier, Patrick Pérez +1
Several works have proposed to learn a two-path neural network that maps images and texts, respectively, to a same shared Euclidean space where geometry captures useful semantic re…
GoSGD: Distributed Optimization for Deep Learning with Gossip Exchange
Michael Blot, David Picard, Matthieu Cord
We address the issue of speeding up the training of convolutional neural networks by studying a distributed method adapted to stochastic gradient descent. Our parallel optimization…