Skip to main content

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

Post a Comment

Popular posts from this blog

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...
Post a Comment
Read more

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.
Post a Comment
Read more