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Found 8 papers, 6 papers with code
Frequency-Based Vulnerability Analysis of Deep Learning Models against Image Corruptions
In response, researchers have developed image corruption datasets to evaluate the performance of deep neural networks in handling such corruptions.
CLAD: A Contrastive Learning based Approach for Background Debiasing
Convolutional neural networks (CNNs) have achieved superhuman performance in multiple vision tasks, especially image classification.
A comment on Guo et al. [arXiv:2206.11228]
In a recent article, Guo et al. [arXiv:2206. 11228] report that adversarially trained neural representations in deep networks may already be as robust as corresponding primate IT neural representations.
Empirical Advocacy of Bio-inspired Models for Robust Image Recognition
There are continuous attempts to use features of the human visual system to improve the robustness of neural networks to data perturbations.
Bio-inspired Robustness: A Review
Many studies have tried to add features of the human visual system to DCNNs to make them robust against adversarial attacks.
A Little Fog for a Large Turn
Small, carefully crafted perturbations called adversarial perturbations can easily fool neural networks.
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models
We analyze the adversarially trained robust models to study their vulnerability against adversarial attacks at the level of the latent layers.
An evaluation metric for object detection algorithms in autonomous navigation systems and its application to a real-time alerting system
An autonomous navigation system relies on a number of sensors including radar, LIDAR and a visible light camera for its operation.
