Day 8 (7/17/19): Incremental Lifelong Learning

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.

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Day 30 (8/16/19): Final Presentations

Today we gave our final presentations, and everyone did a great job. I would like to thank everyone who helped me with this amazing experience! I'm very thankful to have had the opportunity to work on such interesting research with such amazing people this summer.

Day 22 (8/6/19): Streaming Linear Discriminant Analysis

Today I tested the previously trained models using the Stanford dogs dataset as the inter dataset evaluator for OOD instead of the Oxford flowers dataset. However, as expected, the omega values for performance were pretty much the same as before and didn't make much of a difference as the datasets varied. I also implemented a streaming linear discriminant analysis model (SLDA) which differed from the previous incrementally trained models. This model didn't perform as well in terms of accuracy however as only the last layer of the model was trained and streaming is more of a difficult task. Nevertheless, we did show that Mahalanobis can be used in a streaming paradigm to recover some OOD performance in an online setting. This is likely to be a large focus of my presentation as it has never been discussed prior. Tomorrow, I plan to implement an L2SP model with elastic weight consolidation as well as iCarl to serve as two more baselines to compare our experiments to.

Day 24 (8/8/19): Multilayer Perceptron Experiment

I continued gathering more results for my presentation today, and the data table is coming along nicely. We are able to see a significant trend that using Mahalanobis instead of Baseline Thresholding recovers much of the OOD recognition that is lost with streaming or incremental models. The SLDA model appears to be a lightweight, accurate streaming model which can be paired with Mahalanobis to be useful as an embedded agent in the real world. For the purposes of demonstrating catastrophic forgetting, I ran five experiments and averaged the results for a simple incrementally trained MLP. Obviously, the model failed miserably and was achieving only about 1% of the accuracy of the offline model. Including this is only to show how other forms of streaming and incremental models are necessary to develop lifelong learning agents. A diagram of a simple multilayer perceptron.

RIT CIS Summer Internship

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