Search Results for author: Shihan Dou

Found 9 papers, 8 papers with code

On the Universal Adversarial Perturbations for Efficient Data-free Adversarial Detection

1 code implementation27 Jun 2023 Songyang Gao, Shihan Dou, Qi Zhang, Xuanjing Huang, Jin Ma, Ying Shan

Detecting adversarial samples that are carefully crafted to fool the model is a critical step to socially-secure applications.

text-classification Text Classification

DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization

1 code implementation27 Jun 2023 Songyang Gao, Shihan Dou, Yan Liu, Xiao Wang, Qi Zhang, Zhongyu Wei, Jin Ma, Ying Shan

Adversarial training is one of the best-performing methods in improving the robustness of deep language models.

CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing

no code implementations4 May 2023 Songyang Gao, Shihan Dou, Junjie Shan, Qi Zhang, Xuanjing Huang

Dataset bias, i. e., the over-reliance on dataset-specific literal heuristics, is getting increasing attention for its detrimental effect on the generalization ability of NLU models.

Causal Inference Disentanglement +1

Kernel-Whitening: Overcome Dataset Bias with Isotropic Sentence Embedding

1 code implementation14 Oct 2022 Songyang Gao, Shihan Dou, Qi Zhang, Xuanjing Huang

Dataset bias has attracted increasing attention recently for its detrimental effect on the generalization ability of fine-tuned models.

Sentence Embedding Sentence-Embedding

VulCNN: An Image-inspired Scalable Vulnerability Detection System

1 code implementation International Conference on Software Engineering 2022 Yueming Wu, Deqing Zou, Shihan Dou, Wei Yang, Duo Xu, Hai Jin

Furthermore, we conduct a case study on more than 25 million lines of code and the result indicates that VulCNN has the ability to detect large-scale vulnerability.

Image Classification Vulnerability Detection

Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective

2 code implementations COLING 2022 Shihan Dou, Rui Zheng, Ting Wu, Songyang Gao, Junjie Shan, Qi Zhang, Yueming Wu, Xuanjing Huang

Most of the existing debiasing methods often identify and weaken these samples with biased features (i. e., superficial surface features that cause such spurious correlations).

Fact Verification Natural Language Inference +1

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