Day 1 (7/8/19): Introduction

Today was my first day participating in the summer internship program for high school students at the RIT Imaging Center. The morning was spent completing the necessary in-processing such as setting up my computer account, id, and parking permit. After we completed this, the next four hours was spent on a team building exercise consisting of a scavenger hunt all around campus. We created a final video product to present to three judges, and although we didn't achieve as many points as we had hoped, the experience was a lot of fun and a great way to get to know the other interns. Finally, the rest of the day was spent talking with our research lab mentors. I was introduced to the different topics of material I will spend the rest of this week learning including Linear Algebra, Numpy, Computer Vision, Machine Learning, Deep Learning, and Neural Networks. Establishing a solid foundation around these topics will allow me to complete valuable research in the lab in the future. Overall, today was a very exciting introduction to the program, and I'm looking forward to the rest of the summer!

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Day 9 (7/18/19): Incrementally Learning CUB200

Today I continued my work learning about incremental learning models by testing out different strategies on the CUB200 dataset. From what I understand from reading various articles, there seem to be five different approaches to mitigating catastrophic forgetting in lifelong learning models. These are regularization methods (adding constraints to a network's weights), ensemble methods (train multiple classifiers and combine them), rehearsal methods (mix old data with data from the current session), dual-memory methods (based off the human brain, includes a fast learner and a slow learner), and sparse-coding methods (reducing the interference with previously learned representations). All of these methods have their constraints and I don't believe it is yet clear what method (or what combination of different methods) is best. Full rehearsal obviously seems to be the most effective at making the model remember what it had previously learned but given that all training exam...

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.

Day 28 (7/14/19): Presentation Dry Run

In the morning, all of us interns got the chance to practice our presentations in front of each other in the auditorium. I was pretty happy with how mine went overall but the experience was definitely valuable in identifying typos or slight adjustments that should be made. Throughout the rest of the day, I tried to implement these changes and clean up a few plots that I want to include for Friday.

RIT CIS Summer Internship

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