1 code implementation • 27 Mar 2024 • Brian Formento, Wenjie Feng, Chuan Sheng Foo, Luu Anh Tuan, See-Kiong Ng
Language models (LMs) are indispensable tools for natural language processing tasks, but their vulnerability to adversarial attacks remains a concern.
1 code implementation • 29 Feb 2024 • Kennard Yanting Chan, Fayao Liu, Guosheng Lin, Chuan Sheng Foo, Weisi Lin
Lastly, to further improve the training process, FSS proposes a mesh thickness loss signal for pixel-aligned implicit models.
no code implementations • 29 Feb 2024 • Jingyi Liao, Xun Xu, Manh Cuong Nguyen, Adam Goodge, Chuan Sheng Foo
few-shot anomaly detection (FSAD).
1 code implementation • 17 Apr 2023 • Chaoyue Song, Tianyi Chen, YiWen Chen, Jiacheng Wei, Chuan Sheng Foo, Fayao Liu, Guosheng Lin
To solve this problem, we propose neural dual quaternion blend skinning (NeuDBS) to achieve 3D point deformation, which can perform rigid transformation without skin-collapsing artifacts.
1 code implementation • 1 Dec 2022 • Zijian Zhou, Xinyi Xu, Rachael Hwee Ling Sim, Chuan Sheng Foo, Kian Hsiang Low
We observe that the fairness guarantees of exact SVs are too restrictive for SV estimates.
no code implementations • 6 May 2022 • Xun Xu, Jingyi Liao, Lile Cai, Manh Cuong Nguyen, Kangkang Lu, Wanyue Zhang, Yasin Yazici, Chuan Sheng Foo
Recent studies combined finetuning (FT) from pretrained weights with SSL to mitigate the challenges and claimed superior results in the low-label regime.
no code implementations • 2 May 2022 • Xian Shi, Xun Xu, Wanyue Zhang, Xiatian Zhu, Chuan Sheng Foo, Kui Jia
We also demonstrate the feasibility of a more efficient training strategy.
1 code implementation • 17 Dec 2021 • Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, Bryan Kian Hsiang Low
This paper presents a novel collaborative generative modeling (CGM) framework that incentivizes collaboration among self-interested parties to contribute data to a pool for training a generative model (e. g., GAN), from which synthetic data are drawn and distributed to the parties as rewards commensurate to their contributions.
no code implementations • NeurIPS 2021 • Xinyi Xu, Zhaoxuan Wu, Chuan Sheng Foo, Bryan Kian Hsiang Low
We observe that the diversity of the data points is an inherent property of the dataset that is independent of validation.
no code implementations • NeurIPS 2021 • Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low
In this paper, we adopt federated learning as a gradient-based formalization of collaborative machine learning, propose a novel cosine gradient Shapley value to evaluate the agents’ uploaded model parameter updates/gradients, and design theoretically guaranteed fair rewards in the form of better model performance.
no code implementations • 10 Aug 2021 • Balagopal Unnikrishnan, Cuong Nguyen, Shafa Balaram, Chao Li, Chuan Sheng Foo, Pavitra Krishnaswamy
Specifically, we describe adaptations for scenarios with 2D and 3D inputs, uni and multi-label classification, and class distribution mismatch between labeled and unlabeled portions of the training data.
no code implementations • CVPR 2021 • Lile Cai, Xun Xu, Jun Hao Liew, Chuan Sheng Foo
Our results strongly argue for the use of superpixel-based AL for semantic segmentation and highlight the importance of using realistic annotation costs in evaluating such methods.
no code implementations • 18 Jan 2021 • Xian Shi, Xun Xu, Ke Chen, Lile Cai, Chuan Sheng Foo, Kui Jia
Deep learning models are the state-of-the-art methods for semantic point cloud segmentation, the success of which relies on the availability of large-scale annotated datasets.
1 code implementation • 13 Jan 2021 • Govind Narasimman, Kangkang Lu, Arun Raja, Chuan Sheng Foo, Mohamed Sabry Aly, Jie Lin, Vijay Chandrasekhar
Despite the vast literature on Human Activity Recognition (HAR) with wearable inertial sensor data, it is perhaps surprising that there are few studies investigating semisupervised learning for HAR, particularly in a challenging scenario with class imbalance problem.
4 code implementations • 6 Dec 2018 • Houssam Zenati, Manon Romain, Chuan Sheng Foo, Bruno Lecouat, Vijay Ramaseshan Chandrasekhar
Anomaly detection is a significant and hence well-studied problem.
no code implementations • 29 Nov 2018 • Lile Cai, Anne-Maelle Barneche, Arthur Herbout, Chuan Sheng Foo, Jie Lin, Vijay Ramaseshan Chandrasekhar, Mohamed M. Sabry
To this end, we introduce TEA-DNN, a NAS algorithm targeting multi-objective optimization of execution time, energy consumption, and classification accuracy of CNN workloads on embedded architectures.
7 code implementations • 17 Feb 2018 • Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, Vijay Ramaseshan Chandrasekhar
However, few works have explored the use of GANs for the anomaly detection task.