SpectraFlow combines structure-aware pretraining with mask-guided latent alignment and frequency-directional decoding to improve medical image segmentation accuracy and boundary sharpness in low-data regimes.
arXiv preprint arXiv:1911.09071 (2019), https://arxiv.org/abs/1911.090714, 5
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A preprocessor of Gaussian noise plus bilateral filtering yields supralinear adversarial robustness in CNNs and, when paired with adversarial training, ranks near the top of RobustBench while using far less compute, parameters, epochs, and data than prior defenses.
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SpectraFlow: Unifying Structural Pretraining and Frequency Adaptation for Medical Image Segmentation
SpectraFlow combines structure-aware pretraining with mask-guided latent alignment and frequency-directional decoding to improve medical image segmentation accuracy and boundary sharpness in low-data regimes.
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A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs
A preprocessor of Gaussian noise plus bilateral filtering yields supralinear adversarial robustness in CNNs and, when paired with adversarial training, ranks near the top of RobustBench while using far less compute, parameters, epochs, and data than prior defenses.