Day 13 (7/24/19): Offline and Rehearsal Model Experiments

Today I began some experiments that I hope to include in my final project presentation. The main objective as of now is to figure out which incremental learning strategies yield the best out-of-distribution (OOD) performance. For the experiments I performed today, I trained all layers of the models in batches of 20 classes (10 batches for the 200 species in CUB200 dataset) and evaluated OOD using a baseline softmax thresholding method. The performance metrics I hope to obtain are the Omega alpha (how accurate model is compared to offline model) and Omega OOD (how accurate the model is at novelty detection compared to offline model).
*These models are currently still training so I should have the results in the morning.

During lunch I went to the seminar which discussed ASL, specifically how it was important here at RIT. I found the talk very interesting and even learned a few signs which might be useful someday.

Tomorrow I hope to continue my work on this project and expand the experiment to include more complex models using different OOD methods as well.

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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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