Most deep learning models assume ideal conditions and rely on the assumption that test/production data comes from the in-distribution samples from the training data. However, this assumption is not satisfied in most real-world applications. Test data could differ from the training data either due to noise, adversarial perturbations, new classes, or other distribution changes. These shifts in the input data can lead to classifying unknown types, classes that do not appear during training, as known with high confidence. Adversarial perturbations in the input data can cause a sample to be incorrectly classified.
We will discuss approaches based on group-based and individual subset scanning methods from the anomalous pattern detection domain and how they can be applied over off-the-shelf DL models.
Celia Cintas is a Research Scientist at IBM Research Africa - Nairobi, Kenya. She is a member of the AI Science team at the Kenya Lab. Her current research focuses on the improvement of ML techniques to address challenges on Global Health in developing countries and exploring subset scanning for anomaly detection under generative models.
Previously, grantee from National Scientific and Technical Research Council (CONICET) working on Deep Learning and Geometrics Morphometrics for populations studies at LCI-UNS and IPCSH-CONICET (Argentina) as part of the Consortium for Analysis of the Diversity and Evolution of Latin America (CANDELA). During her PhD, she was a visiting student at the University College of London (UK). She was also a Postdoc researcher visitor at Jaén University (Spain) applying ML to Heritage and Archeological studies.
She holds a Ph.D. in Computer Science from Universidad del Sur (Argentina). Co-chair of several Scipy Latinamerica conferences and happy member of LinuxChix Argentina. Financial Aid Co-Chair for the SciPy (USA) Committee (2016-2019) and Diversity Co-Chair for SciPy (2020-2021).