1 code implementation • ICML 2020 • Hai Phan, My T. Thai, Han Hu, Ruoming Jin, Tong Sun, Dejing Dou
In this paper, we aim to develop a scalable algorithm to preserve differential privacy (DP) in adversarial learning for deep neural networks (DNNs), with certified robustness to adversarial examples.
1 code implementation • 6 Nov 2023 • Hai Phan, Cindy Le, Vu Le, Yihui He, Anh Totti Nguyen
DeepFace-EMD (Phan et al. 2022) reaches state-of-the-art accuracy on out-of-distribution data by first comparing two images at the image level, and then at the patch level.
1 code implementation • CVPR 2022 • Hai Phan, Anh Nguyen
Face identification (FI) is ubiquitous and drives many high-stake decisions made by law enforcement.
no code implementations • 5 Dec 2021 • Mark Ralph Baker, Fleur Conway, Filippo Dal Ben, Elizabeth Lucinda Hawthorne, Licia Iacoviello, A Agodi, Saquib Mukhtar, Hai Phan, Yemurai Rabvukwa, Jessica R Rogge
Despite 93. 1% to 95. 8% of the UK adult population having been vaccinated and currently 83. 5% to 89. 8% of adults having received at least two doses (1), and despite many households testing twice a week with lateral flow tests (2), R at the time of writing is 0. 9 to 1. 1, with a growth rate range for England of between -1% and +1% (3).
no code implementations • 29 Sep 2021 • Phung Lai, Hai Phan, Li Xiong, Khang Phuc Tran, My Thai, Tong Sun, Franck Dernoncourt, Jiuxiang Gu, Nikolaos Barmpalios, Rajiv Jain
In this paper, we develop BitRand, a bit-aware randomized response algorithm, to preserve local differential privacy (LDP) in federated learning (FL).
no code implementations • 1 Jan 2021 • Guanxiong Liu, Issa Khalil, Abdallah Khreishah, Hai Phan
In this work, we naturally unify adversarial examples and Trojan backdoors into a new stealthy attack, that is activated only when 1) adversarial perturbation is injected into the input examples and 2) a Trojan backdoor is used to poison the training process simultaneously.
no code implementations • CVPR 2020 • Hai Phan, Zechun Liu, Dang Huynh, Marios Savvides, Kwang-Ting Cheng, Zhiqiang Shen
Inspired by one-shot architecture search frameworks, we manipulate the idea of group convolution to design efficient 1-Bit Convolutional Neural Networks (CNNs), assuming an approximately optimal trade-off between computational cost and model accuracy.
no code implementations • 29 Jul 2019 • Hai Phan, Dang Huynh, Yihui He, Marios Savvides, Zhiqiang Shen
MobileNet and Binary Neural Networks are two among the most widely used techniques to construct deep learning models for performing a variety of tasks on mobile and embedded platforms. In this paper, we present a simple yet efficient scheme to exploit MobileNet binarization at activation function and model weights.
2 code implementations • 4 Dec 2017 • Zhiqiang Shen, Honghui Shi, Jiahui Yu, Hai Phan, Rogerio Feris, Liangliang Cao, Ding Liu, Xinchao Wang, Thomas Huang, Marios Savvides
In this paper, we present a simple and parameter-efficient drop-in module for one-stage object detectors like SSD when learning from scratch (i. e., without pre-trained models).