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Found 5 papers, 1 papers with code
WROOM: An Autonomous Driving Approach for Off-Road Navigation
Off-road navigation is a challenging problem both at the planning level to get a smooth trajectory and at the control level to avoid flipping over, hitting obstacles, or getting stuck at a rough patch.
Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning
In this work, we propose a curriculum learning-based framework by transitioning from a simpler vehicle model to a more complex real environment to teach the reinforcement learning agent a policy closer to the optimal policy.
Towards Adversarial Purification using Denoising AutoEncoders
Since the accuracy and robustness of deep learning models are primarily attributed from the purity of the training samples, therefore the deep learning architectures are often susceptible to adversarial attacks.
Btech thesis report on adversarial attack detection and purification of adverserially attacked images
A deep learning model is trained on certain training examples for various tasks such as classification, regression etc.
Detecting Adversaries, yet Faltering to Noise? Leveraging Conditional Variational AutoEncoders for Adversary Detection in the Presence of Noisy Images
Since the accuracy and robustness of deep learning models are primarily attributed from the purity of the training samples, therefore the deep learning architectures are often susceptible to adversarial attacks.
