Unverified50% confidenceBenchmarkExact time
On the MNIST CNN task, MaRN compressed trainable parameters from 107,998 to 1,872 (approximately 57.7x), while maintaining 91.80% accuracy.
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Long-term
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10/9/2026
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UnverifiedThe handwritten MLP model can achieve approximately 98.5% accuracy on the full MNIST training set.79% similarUnverifiedOn the CNN2-with-pruning task, trainable parameters were compressed to 204, with an accuracy of 81.25%.77% 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).70% 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.69% similarUnverifiedOn the MNIST dataset, even a simple linear classifier can achieve approximately 92% accuracy.68% similar
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