Day 13 (7/24/19): Offline and Rehearsal Model Experiments

Today I began some experiments that I hope to include in my final project presentation. The main objective as of now is to figure out which incremental learning strategies yield the best out-of-distribution (OOD) performance. For the experiments I performed today, I trained all layers of the models in batches of 20 classes (10 batches for the 200 species in CUB200 dataset) and evaluated OOD using a baseline softmax thresholding method. The performance metrics I hope to obtain are the Omega alpha (how accurate model is compared to offline model) and Omega OOD (how accurate the model is at novelty detection compared to offline model).
*These models are currently still training so I should have the results in the morning.

During lunch I went to the seminar which discussed ASL, specifically how it was important here at RIT. I found the talk very interesting and even learned a few signs which might be useful someday.

Tomorrow I hope to continue my work on this project and expand the experiment to include more complex models using different OOD methods as well.

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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 22 (8/6/19): Streaming Linear Discriminant Analysis

Today I tested the previously trained models using the Stanford dogs dataset as the inter dataset evaluator for OOD instead of the Oxford flowers dataset. However, as expected, the omega values for performance were pretty much the same as before and didn't make much of a difference as the datasets varied. I also implemented a streaming linear discriminant analysis model (SLDA) which differed from the previous incrementally trained models. This model didn't perform as well in terms of accuracy however as only the last layer of the model was trained and streaming is more of a difficult task. Nevertheless, we did show that Mahalanobis can be used in a streaming paradigm to recover some OOD performance in an online setting. This is likely to be a large focus of my presentation as it has never been discussed prior. Tomorrow, I plan to implement an L2SP model with elastic weight consolidation as well as iCarl to serve as two more baselines to compare our experiments to.

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.

RIT CIS Summer Internship

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