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 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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