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 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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