Unverified50% confidenceBenchmarkExact time
Under naive training, the network is first trained to 100% accuracy on Task A, then trained only on Task B data, after which Task A accuracy drops from 100% to 64% while Task B reaches 100%.
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50%
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Long-term
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10/9/2026
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UnverifiedIn the EWC experiment, without using any data from Task A, Task A accuracy dips midway to 97% before recovering and stabilizing at 100%, while Task B reaches 100%, achieving no persistent forgetting.75% similarVerifiedIn a reproduction experiment training for 40 epochs on CIFAR-10, plain-56 achieved a training accuracy of 84.0% and plain-20 achieved a training accuracy of 95.1%, even though plain-56's parameter count (853,018) is approximately three times that of plain-20 (269,722).72% similarUnverifiedIn the same reproduction experiment, ResNet-20 achieved a test accuracy of 90.4% and ResNet-56 achieved a test accuracy of 91.7%, with training accuracies of 97.4% and 99.0% respectively.70% similarUnverifiedOn the MNIST CNN task, MaRN compressed trainable parameters from 107,998 to 1,872 (approximately 57.7x), while maintaining 91.80% accuracy.70% similarUnverifiedDuring training, the held-out FID dropped from 33.7 to 27.0, FD-DINOv2 dropped from 570 to 218, and detector-based object accuracy improved from 65% to 90%.69% similar
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