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Found 7 papers, 1 papers with code
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.
Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection
We propose to use an MLP decoder instead of the "deconv-conv" decoder in the mask head, which alleviates the issue and promotes robustness significantly.
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.
