Today I reviewed some of the first true results for the early rounds of experiments I performed. For the offline model (intended to be used as the baseline for the calculating the omega values of incrementally learned models), the final batch of 20 classes yielded an accuracy of 81.20%, an AUROC for Gaussian Noise of .99, an AUROC for Inter datset OOD of .82, and an AUROC for Intra dataset OOD of .80. It is important to note as well that I switched the learning rate scheduler to be exponentially defined rather than decaying the learning rate by steps once it reaches 2/3 of the batch iterations.
The full rehearsal model, as expected, almost performed as well as the offline model achieving an accuracy of 78.12%, an AUROC for Gaussian Noise OOD Omega of .89, an AUROC for Inter datset OOD Omega of .92, and an AUROC for Intra dataset OOD Omega of .98.
It will be interesting to see how these results compare to future models. Most likely, these less memory-intensive models will perform slightly worse than full rehearsal, but it is possible one type of architecture is better suited for this dual lifelong learning task.
The full rehearsal model, as expected, almost performed as well as the offline model achieving an accuracy of 78.12%, an AUROC for Gaussian Noise OOD Omega of .89, an AUROC for Inter datset OOD Omega of .92, and an AUROC for Intra dataset OOD Omega of .98.
It will be interesting to see how these results compare to future models. Most likely, these less memory-intensive models will perform slightly worse than full rehearsal, but it is possible one type of architecture is better suited for this dual lifelong learning task.
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