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Applying adversarial networks to increase the data efficiency and reliability of Self-Driving Cars. (arXiv:2202.07815v1 [cs.CV])
Feb. 17, 2022, 8:20 a.m. | Aakash Kumar
cs.CR updates on arXiv.org arxiv.org
Convolutional Neural Networks (CNNs) are vulnerable to misclassifying images
when small perturbations are present. With the increasing prevalence of CNNs in
self-driving cars, it is vital to ensure these algorithms are robust to prevent
collisions from occurring due to failure in recognizing a situation. In the
Adversarial Self-Driving framework, a Generative Adversarial Network (GAN) is
implemented to generate realistic perturbations in an image that cause a
classifier CNN to misclassify data. This perturbed data is then used to train
the …
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