Day 19 (8/1/19): Analyzing the Results of Early Experiments

Today I reviewed the results of the earlier experiments I ran with my mentor and other students in the lab. The most interesting result (which we most likely will have to repeat to ensure accuracy) was that the intra dataset OOD performance for the full rehearsal model was actually higher than that of the offline model.

The y-axis represents the omega value for intra datset OOD with mahalanobis. The x-axis represents the number of classes learned.

Today was also the RIT Undergraduate Research Symposium which was very fun to attend. Along with a few other interns, I listened to three presentations which talked about political biases affecting article credibility, fingerprinting as a means of cybersecurity defense, and laughter detection and classification using deep learning respectively. Each talk was interesting in its own way, and I enjoyed learning about the other research being performed in a similar field to the one I am working in.

Tomorrow I hope to run more experiments with an SLDA model to see if I achieve similar interesting results.

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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 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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