Day 18 (7/31/19): Obtaining Results from Early Experiments

Today I reviewed some of the first true results for the early rounds of experiments I performed. For the offline model (intended to be used as the baseline for the calculating the omega values of incrementally learned models), the final batch of 20 classes yielded an accuracy of 81.20%, an AUROC for Gaussian Noise of .99, an AUROC for Inter datset OOD of .82, and an AUROC for Intra dataset OOD of .80. It is important to note as well that I switched the learning rate scheduler to be exponentially defined rather than decaying the learning rate by steps once it reaches 2/3 of the batch iterations.

The full rehearsal model, as expected, almost performed as well as the offline model achieving an accuracy of 78.12%, an AUROC for Gaussian Noise OOD Omega of .89, an AUROC for Inter datset OOD Omega of .92, and an AUROC for Intra dataset OOD Omega of .98.

It will be interesting to see how these results compare to future models. Most likely, these less memory-intensive models will perform slightly worse than full rehearsal, but it is possible one type of architecture is better suited for this dual lifelong learning task.

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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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