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 29 (8/15/19): Final Day Before Presentations

Most of today was also spent practicing and editing my presentation to make it as professional as I can. I'm really looking forward to the opportunity to present my work to faculty and friends tomorrow. Here is a link to the slides for my final presentation: Novelty Detection in Streaming Learning using Neural Networks

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.

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...

RIT CIS Summer Internship

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