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Found 19 papers, 10 papers with code
Imperceptible Adversarial Examples in the Physical World
Adversarial examples in the digital domain against deep learning-based computer vision models allow for perturbations that are imperceptible to human eyes.
Robust multi-coil MRI reconstruction via self-supervised denoising
Specifically, employing denoised images rather than noisy counterparts when training DL networks results in lower normalized root mean squared error (NRMSE), higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) across different SNR levels, including 32dB, 22dB, and 12dB for T2-weighted brain data, and 24dB, 14dB, and 4dB for fat-suppressed knee data.
Investigating the Semantic Robustness of CLIP-based Zero-Shot Anomaly Segmentation
Zero-shot anomaly segmentation using pre-trained foundation models is a promising approach that enables effective algorithms without expensive, domain-specific training or fine-tuning.
Enhancing O-RAN Security: Evasion Attacks and Robust Defenses for Graph Reinforcement Learning-based Connection Management
A comprehensive modeling of the security threats and the demonstration of adversarial attacks and defenses on practical AI based O-RAN systems is still in its nascent stages.
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE
Beyond their impressive sampling capabilities, score-based diffusion models offer a powerful analysis tool in the form of unbiased density estimation of a query sample under the training data distribution.
Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Data
We present SURE-Score: an approach for learning score-based generative models using training samples corrupted by additive Gaussian noise.
Automotive RADAR sub-sampling via object detection networks: Leveraging prior signal information
Automotive radar has increasingly attracted attention due to growing interest in autonomous driving technologies.
MIMO Channel Estimation using Score-Based Generative Models
We introduce a framework for training score-based generative models for wireless MIMO channels and performing channel estimation based on posterior sampling at test time.
End-to-end system for object detection from sub-sampled radar data
We show robust detection based on radar data reconstructed using 20% of samples under extreme weather conditions such as snow or fog, and on low-illuminated nights.
Score-Based Generative Models for Robust Channel Estimation
We train a score-based model on channel realizations from the CDL-D model for two antenna spacings and show that the approach leads to competitive in- and out-of-distribution performance when compared to generative adversarial network (GAN) and compressed sensing (CS) methods.
Robust Compressed Sensing MR Imaging with Deep Generative Priors
The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems.
Wideband and Entropy-Aware Deep Soft Bit Quantization
Deep learning has been recently applied to physical layer processing in digital communication systems in order to improve end-to-end performance.
Robust Compressed Sensing MRI with Deep Generative Priors
The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems.
Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization
Accelerated multi-coil magnetic resonance imaging reconstruction has seen a substantial recent improvement combining compressed sensing with deep learning.
EQ-Net: A Unified Deep Learning Framework for Log-Likelihood Ratio Estimation and Quantization
In this work, we introduce EQ-Net: the first holistic framework that solves both the tasks of log-likelihood ratio (LLR) estimation and quantization using a data-driven method.
Robust Face Verification via Disentangled Representations
Our architecture uses a contrastive loss termand a disentangled generative model to sample negative pairs.
Detecting Patch Adversarial Attacks with Image Residuals
We introduce an adversarial sample detection algorithm based on image residuals, specifically designed to guard against patch-based attacks.
Deep Learning-Based Quantization of L-Values for Gray-Coded Modulation
In this work, a deep learning-based quantization scheme for log-likelihood ratio (L-value) storage is introduced.
Deep Log-Likelihood Ratio Quantization
In this work, a deep learning-based method for log-likelihood ratio (LLR) lossy compression and quantization is proposed, with emphasis on a single-input single-output uncorrelated fading communication setting.
