CORDIS Project
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This project focuses on advancing class-incremental learning in deep learning by developing a generative classification approach. Unlike traditional methods, it aims to improve efficiency and scalability by using generative models that learn to classify new classes without requiring simultaneous access to all data.
Learning continually from non-stationary streams of data is a key feature of natural intelligence, but an unsolved problem in deep learning.
Particularly challenging for deep neural networks is the problem of "class-incremental learning", whereby a network must learn to distinguish classes that are not observed together.
In deep learning, the default approach to classification is learning discriminative classifiers.
This works great in the i.i.d. setting when all classes are available simultaneo…
KATHOLIEKE UNIVERSITEIT LEUVEN
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