Search Results for author: Chuan-Sheng Foo

Found 44 papers, 19 papers with code

Waterfall: Framework for Robust and Scalable Text Watermarking

no code implementations5 Jul 2024 Gregory Kang Ruey Lau, Xinyuan Niu, Hieu Dao, Jiangwei Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low

Protecting intellectual property (IP) of text such as articles and code is increasingly important, especially as sophisticated attacks become possible, such as paraphrasing by large language models (LLMs) or even unauthorized training of LLMs on copyrighted text to infringe such IP.

Computational Efficiency

Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions

1 code implementation7 Jun 2024 Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Chuan-Sheng Foo, Bryan Kian Hsiang Low

The increasing complexity of foundational models underscores the necessity for explainability, particularly for fine-tuning, the most widely used training method for adapting models to downstream tasks.

Language Modelling

Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-Training

no code implementations5 May 2024 Wenyu Zhang, Li Shen, Chuan-Sheng Foo

Despite having diverse features important for generalization, the pre-trained feature extractor can overfit to the source data distribution during source training and forget relevant target domain knowledge.

Language Modelling Representation Learning +2

REACTO: Reconstructing Articulated Objects from a Single Video

no code implementations CVPR 2024 Chaoyue Song, Jiacheng Wei, Chuan-Sheng Foo, Guosheng Lin, Fayao Liu

In this paper, we address the challenge of reconstructing general articulated 3D objects from a single video.

Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias

no code implementations CVPR 2024 Wenyu Zhang, Qingmu Liu, Felix Ong Wei Cong, Mohamed Ragab, Chuan-Sheng Foo

UniSSDA is at the intersection of Universal Domain Adaptation (UniDA) and Semi-Supervised Domain Adaptation (SSDA): the UniDA setting does not allow for fine-grained categorization of target private classes not represented in the source domain, while SSDA focuses on the restricted closed-set setting where source and target label spaces match exactly.

Pseudo Label Semi-supervised Domain Adaptation +1

Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior

no code implementations CVPR 2024 Cheng Chen, Xiaofeng Yang, Fan Yang, Chengzeng Feng, Zhoujie Fu, Chuan-Sheng Foo, Guosheng Lin, Fayao Liu

In this paper, we present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model.

3D Generation Text to 3D

WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data

no code implementations1 Oct 2023 Jingtan Wang, Xinyang Lu, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low

The impressive performances of large language models (LLMs) and their immense potential for commercialization have given rise to serious concerns over the intellectual property (IP) of their training data.

Language Modelling Large Language Model

PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation

no code implementations14 Jul 2023 Dapeng Hu, Jian Liang, Xinchao Wang, Chuan-Sheng Foo

The conventional in-domain calibration method, \textit{temperature scaling} (TempScal), encounters challenges due to domain distribution shifts and the absence of labeled target domain data.

Unsupervised Domain Adaptation

Source-Free Domain Adaptation with Temporal Imputation for Time Series Data

1 code implementation14 Jul 2023 Mohamed Ragab, Emadeldeen Eldele, Min Wu, Chuan-Sheng Foo, XiaoLi Li, Zhenghua Chen

The existing SFDA methods that are mainly designed for visual applications may fail to handle the temporal dynamics in time series, leading to impaired adaptation performance.

Imputation Source-Free Domain Adaptation +1

Fair yet Asymptotically Equal Collaborative Learning

1 code implementation9 Jun 2023 Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low

In collaborative learning with streaming data, nodes (e. g., organizations) jointly and continuously learn a machine learning (ML) model by sharing the latest model updates computed from their latest streaming data.

Fairness Incremental Learning

Fourier Sensitivity and Regularization of Computer Vision Models

no code implementations31 Jan 2023 Kiran Krishnamachari, See-Kiong Ng, Chuan-Sheng Foo

Using this result, we propose a general measure of any differentiable model's Fourier-sensitivity using the unitary Fourier-transform of its input-gradient.

Rethinking the Role of Pre-Trained Networks in Source-Free Domain Adaptation

no code implementations ICCV 2023 Wenyu Zhang, Li Shen, Chuan-Sheng Foo

We propose to distil useful target domain information through a co-learning strategy to improve target pseudolabel quality for finetuning the source model.

Representation Learning Source-Free Domain Adaptation +1

Domain Generalization via Selective Consistency Regularization for Time Series Classification

no code implementations16 Jun 2022 Wenyu Zhang, Mohamed Ragab, Chuan-Sheng Foo

Domain generalization methods aim to learn models robust to domain shift with data from a limited number of source domains and without access to target domain samples during training.

Classification Domain Generalization +4

Few-Shot Adaptation of Pre-Trained Networks for Domain Shift

1 code implementation30 May 2022 Wenyu Zhang, Li Shen, Wanyue Zhang, Chuan-Sheng Foo

Recent test-time adaptation methods update batch normalization layers of pre-trained source models deployed in new target environments with streaming data to mitigate such performance degradation.

domain classification Semantic Segmentation +1

SemiCurv: Semi-Supervised Curvilinear Structure Segmentation

1 code implementation18 May 2022 Xun Xu, Manh Cuong Nguyen, Yasin Yazici, Kangkang Lu, Hlaing Min, Chuan-Sheng Foo

In this work, we propose SemiCurv, a semi-supervised learning (SSL) framework for curvilinear structure segmentation that is able to utilize such unlabelled data to reduce the labelling burden.

Data Augmentation Segmentation

ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data

1 code implementation15 Mar 2022 Mohamed Ragab, Emadeldeen Eldele, Wee Ling Tan, Chuan-Sheng Foo, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, XiaoLi Li

Our evaluation includes adapting state-of-the-art visual domain adaptation methods to time series data as well as the recent methods specifically developed for time series data.

Benchmarking Time Series +2

On Automatic Data Augmentation for 3D Point Cloud Classification

1 code implementation11 Dec 2021 Wanyue Zhang, Xun Xu, Fayao Liu, Chuan-Sheng Foo

Data augmentation is an important technique to reduce overfitting and improve learning performance, but existing works on data augmentation for 3D point cloud data are based on heuristics.

3D Object Classification 3D Object Recognition +5

On Representation Knowledge Distillation for Graph Neural Networks

1 code implementation9 Nov 2021 Chaitanya K. Joshi, Fayao Liu, Xu Xun, Jie Lin, Chuan-Sheng Foo

Past work on distillation for GNNs proposed the Local Structure Preserving loss (LSP), which matches local structural relationships defined over edges across the student and teacher's node embeddings.

Contrastive Learning Knowledge Distillation

Selective Cross-Domain Consistency Regularization for Time Series Domain Generalization

no code implementations29 Sep 2021 Wenyu Zhang, Chuan-Sheng Foo, Mohamed Ragab

Domain generalization aims to learn models robust to domain shift, with limited source domains at training and without any access to target domain samples except at test time.

Domain Generalization Representation Learning +3

Source-Free Few-Shot Domain Adaptation

no code implementations29 Sep 2021 Wenyu Zhang, Li Shen, Chuan-Sheng Foo, Wanyue Zhang

Test-time adaptation of pre-trained source models with streaming unlabelled target data is an attractive setting that protects the privacy of source data, but it has mini-batch size and class-distribution requirements on the streaming data which might not be desirable in practice.

domain classification Test-time Adaptation

Spatial Frequency Sensitivity Regularization for Robustness

no code implementations29 Sep 2021 Kiran Chari, Chuan-Sheng Foo, See-Kiong Ng

The ability to generalize to out-of-distribution data is a major challenge for modern deep neural networks.

Global Magnitude Pruning With Minimum Threshold Is All We Need

1 code implementation29 Sep 2021 Manas Gupta, Vishandi Rudy Keneta, Abhishek Vaidyanathan, Ritwik Kanodia, Efe Camci, Chuan-Sheng Foo, Jie Lin

We showcase that magnitude based pruning, specifically, global magnitude pruning (GP) is sufficient to achieve SOTA performance on a range of neural network architectures.

Network Pruning

A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data

no code implementations29 Sep 2021 Mohamed Ragab, Emadeldeen Eldele, Wee Ling Tan, Chuan-Sheng Foo, Zhenghua Chen, Min Wu, Chee Kwoh, XiaoLi Li

Our evaluation includes adaptations of state-of-the-art visual domain adaptation methods to time series data in addition to recent methods specifically developed for time series data.

Benchmarking Model Selection +3

An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series

1 code implementation23 Sep 2021 Astha Garg, Wenyu Zhang, Jules Samaran, Savitha Ramasamy, Chuan-Sheng Foo

Several techniques for multivariate time series anomaly detection have been proposed recently, but a systematic comparison on a common set of datasets and metrics is lacking.

Anomaly Detection Time Series +1

Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis

no code implementations4 Aug 2021 Fayao Liu, Guosheng Lin, Chuan-Sheng Foo, Chaitanya K. Joshi, Jie Lin

In this work we propose PointDisc, a point discriminative learning method to leverage self-supervisions for data-efficient 3D point cloud classification and segmentation.

3D Object Classification 3D Part Segmentation +5

Empirical Analysis of Overfitting and Mode Drop in GAN Training

no code implementations25 Jun 2020 Yasin Yazici, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Vijay Chandrasekhar

We examine two key questions in GAN training, namely overfitting and mode drop, from an empirical perspective.

Scalable and Practical Natural Gradient for Large-Scale Deep Learning

1 code implementation13 Feb 2020 Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota

Large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size.

Image Classification

Learning to Impute: A General Framework for Semi-supervised Learning

2 code implementations22 Dec 2019 Wei-Hong Li, Chuan-Sheng Foo, Hakan Bilen

Recent semi-supervised learning methods have shown to achieve comparable results to their supervised counterparts while using only a small portion of labels in image classification tasks thanks to their regularization strategies.

Classification Facial Landmark Detection +2

Optimistic mirror descent in saddle-point problems: Going the extra(-gradient) mile

no code implementations ICLR 2019 Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras

Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.

Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions

1 code implementation9 Feb 2019 Yasin Yazici, Bruno Lecouat, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, Vijay Chandrasekhar

We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities.

Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials

no code implementations15 Nov 2018 Leo Laugier, Daniil Bash, Jose Recatala, Hong Kuan Ng, Savitha Ramasamy, Chuan-Sheng Foo, Vijay R. Chandrasekhar, Kedar Hippalgaonkar

We introduce the use of Crystal Graph Convolutional Neural Networks (CGCNN), Fully Connected Neural Networks (FCNN) and XGBoost to predict thermoelectric properties.

Attribute

Holistic Multi-modal Memory Network for Movie Question Answering

no code implementations12 Nov 2018 Anran Wang, Anh Tuan Luu, Chuan-Sheng Foo, Hongyuan Zhu, Yi Tay, Vijay Chandrasekhar

In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework which fully considers the interactions between different input sources (multi-modal context, question) in each hop.

Question Answering Retrieval +1

Manifold regularization with GANs for semi-supervised learning

1 code implementation ICLR 2019 Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati, Vijay Chandrasekhar

Generative Adversarial Networks are powerful generative models that are able to model the manifold of natural images.

Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile

no code implementations7 Jul 2018 Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras

Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.

Semi-Supervised Learning with GANs: Revisiting Manifold Regularization

2 code implementations23 May 2018 Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati, Vijay R. Chandrasekhar

GANS are powerful generative models that are able to model the manifold of natural images.

Efficient multiple hyperparameter learning for log-linear models

no code implementations NeurIPS 2007 Chuan-Sheng Foo, Chuong B. Do, Andrew Y. Ng

Using multiple regularization hyperparameters is an effective method for managing model complexity in problems where input features have varying amounts of noise.

Structured Prediction

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