Day 12 (7/23/19): Bounding Classifiers

Today I experimented with different bounded classifiers for open set recognition. A bounding classifier essentially is a type of model which can detect out-of-distribution (OOD) samples, i.e. when presented with an image of a class it has not been trained on. In the image below, a bounded classifier would identify images found in the less dense regions as unknowns rather than try to fit them to a previously-learned class.

I performed my experiment evaluating different bounding classifiers by testing a Resnet50's accuracy in detecting out-of-distribution samples (original dataset is CUB200) either from generated Gaussian noise or the Oxford Flowers dataset. Here are two sample images from those respective dataloaders:

The results I achieved were very similar to those shown in this table (the third row is the CUB200 dataset):

Tomorrow I hope to finally begin to look into the intersection of incremental learning and open set recognition (having experiment with both aspects individually). Then, I will be able to formulate a better idea of the kinds of results I can expect to see for my project.

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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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