no code implementations • 29 Apr 2024 • YuAn Wang, Huazhu Fu, Renuga Kanagavelu, Qingsong Wei, Yong liu, Rick Siow Mong Goh
FedAF inherently avoids the issue of client drift, enhances the quality of condensed data amid notable data heterogeneity, and improves the global model performance.
1 code implementation • 19 Feb 2024 • Wei Jie Yeo, Ranjan Satapathy, Rick Siow Mong Goh, Erik Cambria
We present a comprehensive and multifaceted evaluation of interpretability, examining not only faithfulness but also robustness and utility across multiple commonsense reasoning benchmarks.
no code implementations • 17 Feb 2024 • Hongye Zeng, Ke Zou, Zhihao Chen, Yuchong Gao, Hongbo Chen, Haibin Zhang, Kang Zhou, Meng Wang, Rick Siow Mong Goh, Yong liu, Chang Jiang, Rui Zheng, Huazhu Fu
Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data.
1 code implementation • 3 Jan 2024 • Zitong Huang, Ze Chen, Zhixing Chen, Erjin Zhou, Xinxing Xu, Rick Siow Mong Goh, Yong liu, WangMeng Zuo, ChunMei Feng
When progressing to a new session, pseudo-features are sampled from old-class distributions combined with training images of the current session to optimize the prompt, thus enabling the model to learn new knowledge while retaining old knowledge.
1 code implementation • 19 Dec 2023 • Chun-Mei Feng, Yang Bai, Tao Luo, Zhen Li, Salman Khan, WangMeng Zuo, Xinxing Xu, Rick Siow Mong Goh, Yong liu
By feeding the retrieved image and question to the VQA model, one can find the images inconsistent with relative caption when the answer by VQA is inconsistent with the answer in the QA pair.
1 code implementation • 9 Oct 2023 • Yang Bai, Xinxing Xu, Yong liu, Salman Khan, Fahad Khan, WangMeng Zuo, Rick Siow Mong Goh, Chun-Mei Feng
Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption.
Ranked #1 on Image Retrieval on CIRR
no code implementations • 20 Aug 2023 • Yunlu Yan, Chun-Mei Feng, Yuexiang Li, Rick Siow Mong Goh, Lei Zhu
In this paper, we propose a novel communication-efficient federated learning framework, namely Fed-PMG, to address the missing modality challenge in federated multi-modal MRI reconstruction.
no code implementations • 20 Aug 2023 • Yunlu Yan, Chun-Mei Feng, Mang Ye, WangMeng Zuo, Ping Li, Rick Siow Mong Goh, Lei Zhu, C. L. Philip Chen
Concretely, FedCSD introduces a class prototype similarity distillation to align the local logits with the refined global logits that are weighted by the similarity between local logits and the global prototype.
no code implementations • 9 May 2023 • Myat Thu Linn Aung, Daniel Gerlinghoff, Chuping Qu, Liwei Yang, Tian Huang, Rick Siow Mong Goh, Tao Luo, Weng-Fai Wong
Brain-inspired spiking neural networks (SNNs) replace the multiply-accumulate operations of traditional neural networks by integrate-and-fire neurons, with the goal of achieving greater energy efficiency.
no code implementations • 8 Apr 2023 • Meng Wang, Tian Lin, Lianyu Wang, Aidi Lin, Ke Zou, Xinxing Xu, Yi Zhou, Yuanyuan Peng, Qingquan Meng, Yiming Qian, Guoyao Deng, Zhiqun Wu, Junhong Chen, Jianhong Lin, Mingzhi Zhang, Weifang Zhu, Changqing Zhang, Daoqiang Zhang, Rick Siow Mong Goh, Yong liu, Chi Pui Pang, Xinjian Chen, Haoyu Chen, Huazhu Fu
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies.
no code implementations • 23 Mar 2023 • Meng Wang, Lianyu Wang, Xinxing Xu, Ke Zou, Yiming Qian, Rick Siow Mong Goh, Yong liu, Huazhu Fu
Our TWEU employs an evidential deep layer to produce the uncertainty score with the DR staging results for client reliability evaluation.
2 code implementations • 30 Jan 2023 • Meng Wang, Kai Yu, Chun-Mei Feng, Yiming Qian, Ke Zou, Lianyu Wang, Rick Siow Mong Goh, Yong liu, Huazhu Fu
To the best of our knowledge, our proposed RFedDis is the first work to develop an FL approach based on evidential uncertainty combined with feature disentangling, which enhances the performance and reliability of FL in non-IID domain features.
3 code implementations • 1 Jan 2023 • Ke Zou, Yidi Chen, Ling Huang, Xuedong Yuan, Xiaojing Shen, Meng Wang, Rick Siow Mong Goh, Yong liu, Huazhu Fu
DEviS not only enhances the calibration and robustness of baseline segmentation accuracy but also provides high-efficiency uncertainty estimation for reliable predictions.
no code implementations • 1 Dec 2022 • Meng Wang, Kai Yu, Chun-Mei Feng, Ke Zou, Yanyu Xu, Qingquan Meng, Rick Siow Mong Goh, Yong liu, Huazhu Fu
Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed.
1 code implementation • 10 Nov 2022 • Daniel Gerlinghoff, Tao Luo, Rick Siow Mong Goh, Weng-Fai Wong
Spiking neural networks (SNNs) are a viable alternative to conventional artificial neural networks when resource efficiency and computational complexity are of importance.
1 code implementation • 25 Sep 2022 • Xiaofeng Lei, Shaohua Li, Xinxing Xu, Huazhu Fu, Yong liu, Yih-Chung Tham, Yangqin Feng, Mingrui Tan, Yanyu Xu, Jocelyn Hui Lin Goh, Rick Siow Mong Goh, Ching-Yu Cheng
Therefore, localization has its unique challenges different from segmentation or detection.
no code implementations • 6 Jun 2022 • Daniel Gerlinghoff, Zhehui Wang, Xiaozhe Gu, Rick Siow Mong Goh, Tao Luo
However, current accelerators for SNN cannot well support the emerging encoding schemes.
no code implementations • 1 Dec 2021 • Zhehui Wang, Tao Luo, Rick Siow Mong Goh, Wei zhang, Weng-Fai Wong
In-memory deep learning has already demonstrated orders of magnitude higher performance density and energy efficiency.
1 code implementation • 19 Nov 2021 • Daniel Gerlinghoff, Zhehui Wang, Xiaozhe Gu, Rick Siow Mong Goh, Tao Luo
Compiler frameworks are crucial for the widespread use of FPGA-based deep learning accelerators.
1 code implementation • ICLR 2022 • Jiawei Du, Hanshu Yan, Jiashi Feng, Joey Tianyi Zhou, Liangli Zhen, Rick Siow Mong Goh, Vincent Y. F. Tan
Recently, the relation between the sharpness of the loss landscape and the generalization error has been established by Foret et al. (2020), in which the Sharpness Aware Minimizer (SAM) was proposed to mitigate the degradation of the generalization.
no code implementations • 29 Sep 2021 • Tao Luo, Zhehui Wang, Daniel Gerlinghoff, Rick Siow Mong Goh, Weng-Fai Wong
In this paper, we propose BLUnet, a table lookup-based DNN model with bit-serialized input to overcome this challenge.
1 code implementation • 10 Jul 2021 • Shaohua Li, Xiuchao Sui, Jie Fu, Huazhu Fu, Xiangde Luo, Yangqin Feng, Xinxing Xu, Yong liu, Daniel Ting, Rick Siow Mong Goh
Thus, the chance of overfitting the annotations is greatly reduced, and the model can perform robustly on the target domain after being trained on a few annotated images.
no code implementations • 20 Jun 2021 • Ping Liu, Yuewei Lin, Yang He, Yunchao Wei, Liangli Zhen, Joey Tianyi Zhou, Rick Siow Mong Goh, Jingen Liu
In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection.
1 code implementation • 8 Jun 2021 • Gabriel Tjio, Ping Liu, Joey Tianyi Zhou, Rick Siow Mong Goh
In this work, we propose an adversarial semantic hallucination approach (ASH), which combines a class-conditioned hallucination module and a semantic segmentation module.
no code implementations • Findings (ACL) 2021 • Hao Zhang, Aixin Sun, Wei Jing, Liangli Zhen, Joey Tianyi Zhou, Rick Siow Mong Goh
In this work, we propose a Parallel Attention Network with Sequence matching (SeqPAN) to address the challenges in this task: multi-modal representation learning, and target moment boundary prediction.
1 code implementation • 13 May 2021 • Hao Zhang, Aixin Sun, Wei Jing, Guoshun Nan, Liangli Zhen, Joey Tianyi Zhou, Rick Siow Mong Goh
We adopt the first approach and introduce two contrastive learning objectives to refine video encoder and text encoder to learn video and text representations separately but with better alignment for VCMR.
no code implementations • 26 Feb 2021 • Hao Zhang, Aixin Sun, Wei Jing, Liangli Zhen, Joey Tianyi Zhou, Rick Siow Mong Goh
Our study suggests that the span-based QA framework is an effective strategy to solve the NLVL problem.
1 code implementation • 22 Sep 2020 • Hao Zhang, Joey Tianyi Zhou, Tianying Wang, Ivor W. Tsang, Rick Siow Mong Goh
To facilitate the training of N-ary ECOC with deep learning base learners, we further propose three different variants of parameter sharing architectures for deep N-ary ECOC.
no code implementations • 8 Jun 2020 • Zhehui Wang, Tao Luo, Joey Tianyi Zhou, Rick Siow Mong Goh
EDCompress could also find the optimal dataflow type for specific neural networks in terms of energy consumption, which can guide the deployment of CNN models on hardware systems.
no code implementations • 25 May 2020 • Renuga Kanagavelu, Zengxiang Li, Juniarto Samsudin, Yechao Yang, Feng Yang, Rick Siow Mong Goh, Mervyn Cheah, Praewpiraya Wiwatphonthana, Khajonpong Akkarajitsakul, Shangguang Wangz
Countries across the globe have been pushing strict regulations on the protection of personal or private data collected.
Distributed, Parallel, and Cluster Computing
1 code implementation • 24 Apr 2020 • Jiawei Du, Hanshu Yan, Vincent Y. F. Tan, Joey Tianyi Zhou, Rick Siow Mong Goh, Jiashi Feng
However, similar to existing preprocessing-based methods, the randomized process will degrade the prediction accuracy.
1 code implementation • 12 Apr 2020 • Shaohua Li, Xiuchao Sui, Jie Fu, Yong liu, Rick Siow Mong Goh
To make CNNs more invariant to transformations, we propose "Feature Lenses", a set of ad-hoc modules that can be easily plugged into a trained model (referred to as the "host model").
no code implementations • 17 Mar 2020 • Huafei Zhu, Zengxiang Li, Mervyn Cheah, Rick Siow Mong Goh
In the second fold, an oracle-aided MPC solution for computing weighted federated learning is formalized by decoupling the security of federated learning systems from that of underlying multi-party computations.
Cryptography and Security
no code implementations • 4 Jul 2019 • Shaohua Li, Yong liu, Xiuchao Sui, Cheng Chen, Gabriel Tjio, Daniel Shu Wei Ting, Rick Siow Mong Goh
Deep learning for medical image classification faces three major challenges: 1) the number of annotated medical images for training are usually small; 2) regions of interest (ROIs) are relatively small with unclear boundaries in the whole medical images, and may appear in arbitrary positions across the x, y (and also z in 3D images) dimensions.
no code implementations • ACL 2019 • Joey Tianyi Zhou, Hao Zhang, Di Jin, Hongyuan Zhu, Meng Fang, Rick Siow Mong Goh, Kenneth Kwok
We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER).
no code implementations • ICLR 2019 • Joey Tianyi Zhou, Hao Zhang, Di Jin, Hongyuan Zhu, Rick Siow Mong Goh, Kenneth Kwok
We propose a new architecture termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER).
Low Resource Named Entity Recognition named-entity-recognition +2
no code implementations • 15 Jun 2016 • Zhenzhou Wu, Sunil Sivadas, Yong Kiam Tan, Ma Bin, Rick Siow Mong Goh
Enhancement is achieved by learning a nonlinear mapping function from the features of the corrupted speech signal to that of the reference clean speech signal.
no code implementations • 13 May 2016 • Joey Tianyi Zhou, Xinxing Xu, Sinno Jialin Pan, Ivor W. Tsang, Zheng Qin, Rick Siow Mong Goh
Specifically, we extend the standard learning to hash method, Iterative Quantization (ITQ), in a transfer learning manner, namely ITQ+.
no code implementations • 6 Apr 2016 • Xinxing Xu, Joey Tianyi Zhou, IvorW. Tsang, Zheng Qin, Rick Siow Mong Goh, Yong liu
The Support Vector Machine using Privileged Information (SVM+) has been proposed to train a classifier to utilize the additional privileged information that is only available in the training phase but not available in the test phase.