Publications associées à ImageNet

Differentiable Patch Selection for Image Recognition

Jean-Baptiste Francis Marie Juliette Cordonnier, Aravindh Mahendran

Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand. We propose a method based on a differentiable Top-K operator to select the ...

IEEE COMPUTER SOC2021

Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Martin Jaggi, Sebastian Urban Stich, Tao Lin, Sai Praneeth Reddy Karimireddy

Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization ch ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Trainable Preprocessing for Reduced Precision Neural Networks

Gabor Andras Csordas

Applications of neural networks are emerging in many fields and are frequently implemented in embedded environment, introducing power, throughput and latency constraints next to accuracy. Although practical computer vision solutions always involve some kin ...

EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP2021

Pretraining boosts out-of-domain robustness for pose estimation

Alexander Mathis, Mackenzie Mathis, Steffen Schneider, Matthias Bethge, Thomas Ray Biasi, Mert Yüksekgönül

Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe ...

IEEE COMPUTER SOC2021

Masksembles for Uncertainty Estimation

Pascal Fua, Pierre Bruno Baqué, Nikita Durasov, Timur Bagautdinov

Deep neural networks have amply demonstrated their prowess but estimating the reliability of their predictions remains challenging. Deep Ensembles are widely considered as being one of the best methods for generating uncertainty estimates but are very expe ...

2021

Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery

Devis Tuia, Diego Marcos Gonzalez

While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day. In order to leverage these data to learn an image representation more adequate for change ...

Springer, Cham2021

AIDE: Accelerating image‐based ecological surveys with interactive machine learning

Devis Tuia, Benjamin Alexander Kellenberger

Ecological surveys increasingly rely on large‐scale image datasets, typically terabytes of imagery for a single survey. The ability to collect this volume of data allows surveys of unprecedented scale, at the cost of expansive volumes of photo‐interpretati ...

2020

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Francesco Croce

A large body of research has focused on adversarial attacks which require to modify all input features with small l2- or l∞-norms. In this paper we instead focus on query-efficient sparse attacks in the black-box setting. Our versatile framework, Sparse-RS ...

2020

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ImageNet

Differentiable Patch Selection for Image Recognition

Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Trainable Preprocessing for Reduced Precision Neural Networks

Pretraining boosts out-of-domain robustness for pose estimation

Masksembles for Uncertainty Estimation

Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery

Conditional LSTM-GAN for Melody Generation from Lyrics

From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search

AIDE: Accelerating image‐based ecological surveys with interactive machine learning

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Graph Chatbot

Chattez avec Graph Search

Pretraining boosts out-of-domain robustness for pose estimation

Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery

From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Trainable Preprocessing for Reduced Precision Neural Networks

Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Masksembles for Uncertainty Estimation

Conditional LSTM-GAN for Melody Generation from Lyrics

AIDE: Accelerating image‐based ecological surveys with interactive machine learning

Differentiable Patch Selection for Image Recognition