FPCNet uses an encoder-decoder architecture with MD and SEU modules to learn multi-context crack features and achieves faster, more accurate pixel-level detection than prior methods on CFD and G45 datasets.
Gradient-based learning applied to document recognition,
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Proposes a self-aware unlearning method inspired by hypnopaedia that uses model inversion and hypothesis testing to detect and detach backdoor triggers from machine learning models.
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FPCNet: Fast Pavement Crack Detection Network Based on Encoder-Decoder Architecture
FPCNet uses an encoder-decoder architecture with MD and SEU modules to learn multi-context crack features and achieves faster, more accurate pixel-level detection than prior methods on CFD and G45 datasets.
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Hypnopaedia-Aware Machine Unlearning via Psychometrics of Artificial Mental Imagery
Proposes a self-aware unlearning method inspired by hypnopaedia that uses model inversion and hypothesis testing to detect and detach backdoor triggers from machine learning models.