Day 17 (7/30/19): Adding Bounding Boxes in Experiments

Today was very successful as we finally were able to finish debugging the script we were using for training our experiment models. Previously, I was seeing the loss function diverge to nan during the second batch and the problem was actually that I was forgetting to shuffle the data with each new batch. After cleaning up much of the script, I was able to organize the code to be more general purpose for future experiments.

I ran the new code to train an offline so i could generate some baseline numbers to use for omega values for the experiments. However, I am still adjusting a few hyperparameters (namely the patience counter for early stopping to prevent overfitting) to see what will yield the highest offline accuracy with bounding boxes implemented.

The full rehearsal model was fairly simple to implement after the offline model trained so I plan to start evaluating Mahalanobis OOD and more complex models tomorrow.

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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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