Today I reran the experiments from yesterday with the dataloaders for the OOD performance evaluation having equal in- and out-loader sample sizes. Theoretically, this would lead to a more accurate AUROC metric. However, just glancing at a visualization of the new results, it appears that we are achieving the same interesting results as yesterday. Unsure of the underlying reason why, I hope to plot the metrics we calculated on the same set of axis to get a better representation of the results.
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...
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