Search Results for author: Chuan Sheng Foo

Found 17 papers, 8 papers with code

TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks

no code implementations29 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.

General Classification Image Classification +1

A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition

1 code implementation13 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.

Human Activity Recognition

Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach

no code implementations18 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.

Active Learning Benchmarking +3

Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs

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.

Active Learning Semantic Segmentation +1

Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean

no code implementations10 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.

Classification Image Classification +1

Validation Free and Replication Robust Volume-based Data Valuation

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.

Data Valuation

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

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.

BIG-bench Machine Learning Fairness +1

Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards

1 code implementation17 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.

BIG-bench Machine Learning Data Valuation +1

Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime

no code implementations6 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.

MoDA: Modeling Deformable 3D Objects from Casual Videos

1 code implementation17 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.

Fine Structure-Aware Sampling: A New Sampling Training Scheme for Pixel-Aligned Implicit Models in Single-View Human Reconstruction

1 code implementation29 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.

SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks

1 code implementation27 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.

Word Embeddings

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