Search Results for author:
Found 8 papers, 1 papers with code
Online Unsupervised Domain Adaptation for Person Re-identification
Therefore, we present a new yet practical online setting for Unsupervised Domain Adaptation for person Re-ID with two main constraints: Online Adaptation and Privacy Protection.
Online unsupervised domain adaptation
Person Re-Identification
Prediction of fish location by combining fisheries data and sea bottom temperature forecasting
This paper combines fisheries dependent data and environmental data to be used in a machine learning pipeline to predict the spatio-temporal abundance of two species (plaice and sole) commonly caught by the Belgian fishery in the North Sea.
Using Synthetic Corruptions to Measure Robustness to Natural Distribution Shifts
Using the overlapping criterion, we split synthetic corruptions into categories that help to better understand neural network robustness.
Using the Overlapping Score to Improve Corruption Benchmarks
To estimate the robustness of neural networks to these common corruptions, we generally use a group of modeled corruptions gathered into a benchmark.
Increasing the Coverage and Balance of Robustness Benchmarks by Using Non-Overlapping Corruptions
In this paper, we propose to build corruption benchmarks with only non-overlapping corruptions, to improve their coverage and their balance.
Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training
Despite their performance, Artificial Neural Networks are not reliable enough for most of industrial applications.
Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?
We intend to study the links between the robustnesses of neural networks to both perturbations.
Person re-identification across different datasets with multi-task learning
We show that with our method, we are able to build a system that performs well on different datasets and simultaneously extracts attributes.
