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Found 9 papers, 5 papers with code
DAD++: Improved Data-free Test Time Adversarial Defense
With the increasing deployment of deep neural networks in safety-critical applications such as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness has become a crucial concern in the reliability of these networks in real-world scenarios.
Adversarial Adaptation for French Named Entity Recognition
We propose a Transformer-based NER approach for French, using adversarial adaptation to similar domain or general corpora to improve feature extraction and enable better generalization.
Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora
Named Entity Recognition (NER) involves the identification and classification of named entities in unstructured text into predefined classes.
An Emotion-guided Approach to Domain Adaptive Fake News Detection using Adversarial Learning
Recent works on fake news detection have shown the efficacy of using emotions as a feature for improved performance.
Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation
Recent works on fake news detection have shown the efficacy of using emotions as a feature or emotions-based features for improved performance.
An Emotion-Aware Multi-Task Approach to Fake News and Rumour Detection using Transfer Learning
We further analyze the performance of our multi-task approach for fake news detection in cross-domain settings to verify its efficacy for better generalization across datasets, and to verify that emotions act as a domain-independent feature.
A Spreader Ranking Algorithm for Extremely Low-budget Influence Maximization in Social Networks using Community Bridge Nodes
In this work, we propose a community structures-based approach, which employs a K-Shell algorithm in order to generate a score for the connections between seed nodes and communities for low-budget scenarios.
Robust Few-shot Learning Without Using any Adversarial Samples
These methods rely on the generation of adversarial samples in every episode of training, which further adds a computational burden.
Data-free Defense of Black Box Models Against Adversarial Attacks
At test time, WNR combined with trained regenerator network is prepended to the black box network, resulting in a high boost in adversarial accuracy.
