Day 15 (7/26/19): Testing Models with Rehearsal and L2-SP Regularization

Today I continued the experiments from yesterday along with implementing a L2SP Model and Partial Rehearsal with Baseline OOD. So far it seems that the performance of every model (both accuracy and area under ROC curve) significantly drops as the number of classes learned increases. Implementing the more complex models such as SLDA, S-SVM, and L2SP (EWC) as well as more accurate inference methods such as Mahalanobis will be a challenge but also interesting to see how well they perform.

The blue line represents the L2SP model and the red line represents the Full Rehearsal model. These have only been trained for around three batches of 20 classes and will continue to learn overnight. However, the performance trend will most likely continue as the accuracy drops with newly added classes.

I also finished my presentation outline today which can be found at this link: RIT Presentation Outline

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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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