Unverified50% confidenceBenchmarkExact time
Compared with PGD-based adversarial training, this method achieves a relative improvement in robust accuracy of at least 20.58% under PGD attacks and a relative improvement of 10.20% in classification accuracy on clean samples.
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50%
Confidence
Medium-term (~90 days)
Relevance
10/27/2026
First Seen
Valid until: 1/25/2027
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VerifiedIn 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% 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 reduced experiments using only 20%, 40%, and 80% of the training data, the performance gains persist stably, confirming the reliability of this method in low-resource settings.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% similarUnverifiedExperiments in the paper show that while maintaining competitive predictive performance, CausalCoT improves the causal sensitivity of intermediate reasoning steps by an average of 12 percentage points over the strongest baseline, with a maximum improvement of 21 percentage points.69% similar
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