CPNS regularization with dual counterfactual generators mitigates intra-task and inter-task spurious correlations in class-incremental learning feature expansion.
Learning multiple layers of features from tiny images
2 Pith papers cite this work. Polarity classification is still indexing.
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AdVAR-DNN employs a variational autoencoder to create untraceable adversarial samples that compromise black-box collaborative DNN inference by exploiting model partitioning information exchange, achieving high misclassification success on CIFAR-100 with low detection probability.
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Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning
CPNS regularization with dual counterfactual generators mitigates intra-task and inter-task spurious correlations in class-incremental learning feature expansion.
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Variational Autoencoder-Based Black-Box Adversarial Attack on Collaborative DNN Inference
AdVAR-DNN employs a variational autoencoder to create untraceable adversarial samples that compromise black-box collaborative DNN inference by exploiting model partitioning information exchange, achieving high misclassification success on CIFAR-100 with low detection probability.