I spent the majority of today reading recent papers outlining various approaches to achieving effective incremental learning deep learning models. There seems to be a wide variety of proposed systems with no general consensus on which is best or how to evaluate the different models. In fact, incremental learning does not always mean the same thing in different papers because some models incrementally learn classes while others incrementally learn datasets or even stray from the batch setting altogether by learning from streaming data. As a result there does not yet exist a standardized way of evaluating which models are actually best at achieving lifelong learning because they are often tested on significantly different tasks.
After lunch, my advisor and I went through a presentation made by Tyler Hayes, another PhD student in the lab. It discussed many of the problems and proposed solutions which I was reading about and explained the focus of a lot of the research the kLab is doing.
Tomorrow I plan to spend the day trying to implement incremental learning into my own model to classify the CUB200 dataset. It should be challenging but also rewarding once I manage to finish it.
After lunch, my advisor and I went through a presentation made by Tyler Hayes, another PhD student in the lab. It discussed many of the problems and proposed solutions which I was reading about and explained the focus of a lot of the research the kLab is doing.
Tomorrow I plan to spend the day trying to implement incremental learning into my own model to classify the CUB200 dataset. It should be challenging but also rewarding once I manage to finish it.
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