Day 21 (8/5/19): Averaging Experiments

This morning I was at a basketball camp so I came into the lab around noon. Much of the day was spent waiting for some models to finish training to I worked on adding some slides to my presentation document.

In the afternoon, I got back results from models that I could average together to get more accurate results. The general trend remained the same however which indicated that the Mahalanobis Intra Dataset OOD actually performed better when it was trained incrementally (albeit with full rehearsal) than when it was trained offline. I am not sure yet what the reason for this is, but I will continue to look into it.

The green line denotes the intra dataset mahalanobis OOD omega for full rehearsal. Note how it consistently is above 1 even as more batches are learned incrementally.

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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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