Continual Generative Models


To cope with real-world dynamics, an intelligent agent needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as continual learning, provides a foundation for AI systems to develop themselves adaptively. In a general sense, continual learning is explicitly limited by catastrophic forgetting, where learning a new task usually results in a dramatic performance drop of the old tasks. Beyond this, increasingly numerous advances have emerged in recent years that largely extend the understanding and application of continual learning. The growing and widespread interest in this direction demonstrates its realistic significance as well as complexity. 

In this work, students will be asked to perform a comprehensive survey of continual learning, seeking to bridge the basic settings of classification framework toward generative models, and try some basic steps to implement continual generative models!

This project is suitable for (but not limited to) students majoring in electrical engineering, computer science, industrial engineering, and mathematics. Ideally, students in their third year or higher are preferred. Basic English skills for reading and presenting papers and proficiency in Python programming are required. Otherwise, it will be very difficult to proceed with the project. Students who have experience with deep learning projects or have read papers on the topic are preferred. If they have experience working with generative models, it would be even better.

Supervisors

(If you are interested, please contact the supervisor below)


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