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Curved Representation Space of Vision Transformers
In this paper, we focus on the phenomenon that Transformers show higher robustness against corruptions than CNNs, while not being overconfident.
Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective
This paper proposes an additional perspective that explores the adversarial robustness of Transformers against frequency-selective perturbations in the spectral domain.
Amicable Aid: Perturbing Images to Improve Classification Performance
While adversarial perturbation of images to attack deep image classification models pose serious security concerns in practice, this paper suggests a novel paradigm where the concept of image perturbation can benefit classification performance, which we call amicable aid.
Designing a Robust Carrier Frequency Offset Estimation Scheme for Meeting Target Decoding Performance in an OFDM System
In a target communication system, a delicately designed frequency offset estimation scheme is required to meet certain decoding performance.
Exploitation of Channel-Learning for Enhancing 5G Blind Beam Index Detection
The cell search is an essential function for a User Equipment (UE) to be initially associated with a base station, and is also important to further maintain the wireless connection.
