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Found 12 papers, 3 papers with code
Mitigating Hallucinations in Large Vision-Language Models by Adaptively Constraining Information Flow
Furthermore, we propose an entropy-based noise-controlling strategy to enable the injected noise to be adaptively constrained regarding the smoothness of the similarity distribution.
Neural Honeytrace: A Robust Plug-and-Play Watermarking Framework against Model Extraction Attacks
Guided by the model, we further introduce: (1) a similarity-based training-free watermarking method for plug-and-play and flexible watermarking, and (2) a distribution-based multi-step watermark information transmission strategy for robust watermarking.
Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field
In this paper, we attribute the inadequate imperceptibility of adversarial attacks on point clouds to deviations from the underlying surface.
CORES: Convolutional Response-based Score for Out-of-distribution Detection
Deep neural networks (DNNs) often display overconfidence when encountering out-of-distribution (OOD) samples posing significant challenges in real-world applications.
PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks
To address this imbalance, an intrusion detection system called pretraining Wasserstein generative adversarial network intrusion detection system (PWG-IDS) is proposed in this paper.
Stabilized Likelihood-based Imitation Learning via Denoising Continuous Normalizing Flow
State-of-the-art imitation learning (IL) approaches, e. g, GAIL, apply adversarial training to minimize the discrepancy between expert and learner behaviors, which is prone to unstable training and mode collapse.
TREATED:Towards Universal Defense against Textual Adversarial Attacks
Recent work shows that deep neural networks are vulnerable to adversarial examples.
CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue
Overconfident predictions on out-of-distribution (OOD) samples is a thorny issue for deep neural networks.
Generative Adversarial Network
Out of Distribution (OOD) Detection
PRGAN: Personalized Recommendation with Conditional Generative Adversarial Networks
However, recommender systems suffer from interaction data sparsity and data noise problems in reality.
Decision Propagation Networks for Image Classification
High-level (e. g., semantic) features encoded in the latter layers of convolutional neural networks are extensively exploited for image classification, leaving low-level (e. g., color) features in the early layers underexplored.
