Raphaël BAENA Thesis defense

Address:

IMT Atlantique - Campus de Brest - Salle B02-007A

Mr Raphaël Baena from Mathematical and Electrical Engineering (MEE) Department and the LabSTICC laboratory, will present his research about :

"Beyond the training task in classification : looking at extensions of the notion of generalization"

 

Thesis defense Notice

 

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The thesis focuses on the concept of generalization, particularly in the context of supervised machine learning classification. This approach involves learning to solve a task (classification) based on labeled training data. Generalization is defined as the ability to make
accurate predictions on unseen data during training. Traditionally, generalization is limited to data that belongs to the same domain as the training task. However, recent literature highlights the capability of deep learning architectures to generalize beyond their training task. Thus, a model trained on a specific task can be partially reused for other tasks. The thesis explores various possible extensions of generalization, including learning on a set of classes and the ability to generalize to a larger set of classes, learning on coarse labels to predict more complex labels, learning on artificially complex tasks to improve generalization, and learning invariant operators for a specific task.

Organizer(s)

Thesis acreditation from IMT Atlantique with the Doctoral School SPIN

 

Keywords Deep Learning, Classification, Supervised Learning, Transfer Learning, Generalization

Published on 24.11.2023
 
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