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 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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