Day 16 (7/29/19): Achieving Better Results by Adjusting Learning Rate

This morning I reviewed the results of the experiments I ran over the weekend. However, the tensorboard graphs showed that after a certain number of batches, every model appeared to stop learning on the training data, essentially becoming stagnant at the end. Below is a graphical representation of those models over the weekend.

I realized that the training script I was using didn't explicitly reset the optimizer and learning rate scheduler after each batch as I intended. Therefore, as the learning rate was continuously getting smaller, the model was no longer learning in the further batches. I adjusted my script to reset the parameters of both the optimizer and scheduler after each batch of 20 classes and restarted many of the experiments. I also added some more capabilities to load and save the models by creating dictionaries to track how the accuracy and omega values (against default values of offline model) change as the number of batches and classes increase. I hope to have results from the Offline, Full Rehearsal, and L2SP models with Baseline OOD Recognition tomorrow.

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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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