A framework models DNN layer weight-activation interactions via Bernoulli distributions and uses class separation as a diagnostic proxy to quantify distributional robustness, tested on CIFAR-10 and ImageNet models.
Detection of out-of-distribution samples using binary neuron activation patterns.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3378–3387, 2022
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A New Framework to Analyse the Distributional Robustness of Deep Neural Networks
A framework models DNN layer weight-activation interactions via Bernoulli distributions and uses class separation as a diagnostic proxy to quantify distributional robustness, tested on CIFAR-10 and ImageNet models.