Winarto AA
Explaining adversarial vulnerabiity with a data sparisty hypothesis
Keywords : Adversarial vulnerability, Adversarial attacks, Decision boundary, Regularization, Data Sparsity, Smooth SoftMax score surface
ReadExplaining adversarial vulnerabiity with a data sparisty hypothesis
Despite many proposed algorithms to provide robustness to deep learning (DL) models, DL models remain susceptible to adversarial attacks. We hypothesize that the adversarial vulnerability of DL models stems from two factors. The first factor is data sparsity which is that in the high dimensional input data space, there exist large regions outside the support of the data distribution. The second factor is the existence of many redundant parameters in the DL models. Owing to these factors, different models are able to come up with different decision boundaries with comparably high prediction accuracy.
ReadAutomated grading of acne vulgaris by deep learning with convolutional neural networks
BACKGROUND: The visual assessment and severity grading of acne vulgaris by physicians can be subjective, resulting in inter- and intra-observer variability. OBJECTIVE: To develop and validate an algorithm for the automated calculation of the Investigator's Global Assessment (IGA) scale, to standardize acne severity and outcome measurements.
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