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 30 (8/16/19): Final Presentations

Today we gave our final presentations, and everyone did a great job. I would like to thank everyone who helped me with this amazing experience! I'm very thankful to have had the opportunity to work on such interesting research with such amazing people this summer.

Day 24 (8/8/19): Multilayer Perceptron Experiment

I continued gathering more results for my presentation today, and the data table is coming along nicely. We are able to see a significant trend that using Mahalanobis instead of Baseline Thresholding recovers much of the OOD recognition that is lost with streaming or incremental models. The SLDA model appears to be a lightweight, accurate streaming model which can be paired with Mahalanobis to be useful as an embedded agent in the real world. For the purposes of demonstrating catastrophic forgetting, I ran five experiments and averaged the results for a simple incrementally trained MLP. Obviously, the model failed miserably and was achieving only about 1% of the accuracy of the offline model. Including this is only to show how other forms of streaming and incremental models are necessary to develop lifelong learning agents. A diagram of a simple multilayer perceptron.

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.

RIT CIS Summer Internship

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