1 code implementation • ALTA 2021 • Xinzhe Li, Ming Liu, Xingjun Ma, Longxiang Gao
Universal adversarial texts (UATs) refer to short pieces of text units that can largely affect the predictions of NLP models.
1 code implementation • 30 Nov 2023 • Xinzhe Li, Sun Rui, Yiming Niu, Yao Liu
Specifically, the framework consists of a precipitation predictor with multiple lightweight heads (learners) and a controller that combines the outputs from these heads.
no code implementations • 22 Oct 2023 • Yong Du, Jiahui Zhan, Shengfeng He, Xinzhe Li, Junyu Dong, Sheng Chen, Ming-Hsuan Yang
In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences.
1 code implementation • 2 Jul 2023 • Xinzhe Li, Ming Liu, Shang Gao
This paper addresses the ethical concerns arising from the use of unauthorized public data in deep learning models and proposes a novel solution.
1 code implementation • 27 Jun 2023 • Xinzhe Li, Ming Liu, Shang Gao
For Pretrained Language Models (PLMs), their susceptibility to noise has recently been linked to subword segmentation.
no code implementations • 27 Jun 2023 • Xinzhe Li, Ming Liu, Shang Gao, Wray Buntine
Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models.
1 code implementation • NeurIPS 2019 • Xinzhe Li, Qianru Sun, Yaoyao Liu, Shibao Zheng, Qin Zhou, Tat-Seng Chua, Bernt Schiele
On each task, we train a few-shot model to predict pseudo labels for unlabeled data, and then iterate the self-training steps on labeled and pseudo-labeled data with each step followed by fine-tuning.
no code implementations • 1 Apr 2018 • Qin Zhou, Heng Fan, Shibao Zheng, Hang Su, Xinzhe Li, Shuang Wu, Haibin Ling
In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification.