X-GAN: Improving Generative Adversarial Networks with ConveX Combinations

Blum, Oliver; Brattoli, Biagio; Ommer, Björn

doi:10.1007/978-3-030-12939-2_15

Cited by 3 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given a clean (unperturbed) input x a targeted sparse adversarial attack aims for finding a perturbation so that the perturbed input x is incorrectly classified to a target class [21]. This attack method has been used in numerous research studies [21], [70], [76]- [80]. For example, in [21]…”

Section: Breaching Security By Improving Attacksmentioning

confidence: 99%

Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions

et al. 2022

View full text Add to dashboard Cite

Recent natural language processing (NLP) techniques have accomplished high performance on benchmark data sets, primarily due to the significant improvement in the performance of deep learning. The advances in the research community have led to great enhancements in state-of-the-art production systems for NLP tasks, such as virtual assistants, speech recognition, and sentiment analysis. However, such NLP systems still often fail when tested with adversarial attacks. The initial lack of robustness exposed troubling gaps in current models' language understanding capabilities, creating problems when NLP systems are deployed in real life. In this paper, we present a structured overview of NLP robustness research by summarizing the literature in a systemic way across various dimensions. We then take a deepdive into the various dimensions of robustness, across techniques, metrics, embedding, and benchmarks. Finally, we argue that robustness should be multi-dimensional, provide insights into current research, identify gaps in the literature to suggest directions worth pursuing to address these gaps INDEX TERMS Natural Language Processing; Adversarial Attacks; Robustness.

show abstract

Section: Breaching Security By Improving Attacksmentioning

confidence: 99%

Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions

et al. 2022

View full text Add to dashboard Cite

show abstract

Section: Breaching Security By Improving Attacksmentioning

confidence: 99%

Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions

Omar¹,

Choi²,

Nyang³

et al. 2022

Preprint

View full text Add to dashboard Cite

Recent natural language processing (NLP) techniques have accomplished high performance on benchmark datasets, primarily due to the significant improvement in the performance of deep learning. The advances in the research community have led to great enhancements in state-of-the-art production systems for NLP tasks, such as virtual assistants, speech recognition, and sentiment analysis. However, such NLP systems still often fail when tested with adversarial attacks. The initial lack of robustness exposed troubling gaps in current models' language understanding capabilities, creating problems when NLP systems are deployed in real life. In this paper, we present a structured overview of NLP robustness research by summarizing the literature in a systemic way across various dimensions. We then take a deep-dive into the various dimensions of robustness, across techniques, metrics, embeddings, and benchmarks. Finally, we argue that robustness should be multi-dimensional, provide insights into current research, identify gaps in the literature to suggest directions worth pursuing to address these gaps.

show abstract

Adversarial attacks and defenses for large language models (LLMs): methods, frameworks & challenges

Kumar

2024

Int J Multimed Info Retr

View full text Add to dashboard Cite

X-GAN: Improving Generative Adversarial Networks with ConveX Combinations

Cited by 3 publications

References 14 publications

Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions

Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions

Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions

Adversarial attacks and defenses for large language models (LLMs): methods, frameworks & challenges

Contact Info

Product

Resources

About