Introduces ImageNet-C and ImageNet-P benchmarks revealing negligible robustness gains from AlexNet to ResNet models on common corruptions and perturbations, plus methods to improve them.
The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Maximum softmax probability acts as a baseline for detecting misclassified and out-of-distribution examples in neural networks.
citing papers explorer
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Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Introduces ImageNet-C and ImageNet-P benchmarks revealing negligible robustness gains from AlexNet to ResNet models on common corruptions and perturbations, plus methods to improve them.
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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Maximum softmax probability acts as a baseline for detecting misclassified and out-of-distribution examples in neural networks.