Out-of-Distribution Learning

Out-of-Distribution learning is concerned with developing methods where the distribution of the data processed at test time may be different from the distrib...

Novelty and Anomaly Detection

Detecting the presence of outliers, like novelties or anomalies, with respect to a particular distribution has numerous applications in computer vision and i...


Transfer Learning from Multiple Sources

2 minute read

Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the availab...