Day 25 (8/9/19): Finishing Presentation

Today I made a lot of progress finishing up my presentation. I feel like we developed an interesting story to tell around the data we collected from the experiments, and I am excited to get a chance to share my results. Much of the beginning of the presentation is spent explaining high level concepts such as deep learning and machine learning so I will have a better idea of what I will need to include after my meeting with Joe and Amy on Monday. I will continue to keep practicing my presentation over the weekend and possible include more results from iCaRL and MLP w/ EWC models if I can get them trained. Below I have included a visualization of one of the most important results from my project. Notice how the SLDA w/ Mahalanobis model outperforms the other models in accuracy and OOD recognition combined (the more area a model has in the spider plot, the better it performed overall).

Comments

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

Search This Blog