Search Results for author: Jiakai Wang

Found 19 papers, 13 papers with code

D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models

no code implementations3 Jun 2024 Haoran Que, Jiaheng Liu, Ge Zhang, Chenchen Zhang, Xingwei Qu, Yinghao Ma, Feiyu Duan, Zhiqi Bai, Jiakai Wang, Yuanxing Zhang, Xu Tan, Jie Fu, Wenbo Su, Jiamang Wang, Lin Qu, Bo Zheng

To address the limitations of existing methods, inspired by the Scaling Law for performance prediction, we propose to investigate the Scaling Law of the Domain-specific Continual Pre-Training (D-CPT Law) to decide the optimal mixture ratio with acceptable training costs for LLMs of different sizes.

Math

Towards Accurate Binarization of Diffusion Model

1 code implementation8 Apr 2024 Xingyu Zheng, Haotong Qin, Xudong Ma, Mingyuan Zhang, Haojie Hao, Jiakai Wang, Zixiang Zhao, Jinyang Guo, Xianglong Liu

With the advancement of diffusion models (DMs) and the substantially increased computational requirements, quantization emerges as a practical solution to obtain compact and efficient low-bit DMs.

Binarization Quantization

E^2-LLM: Efficient and Extreme Length Extension of Large Language Models

no code implementations13 Jan 2024 Jiaheng Liu, Zhiqi Bai, Yuanxing Zhang, Chenchen Zhang, Yu Zhang, Ge Zhang, Jiakai Wang, Haoran Que, Yukang Chen, Wenbo Su, Tiezheng Ge, Jie Fu, Wenhu Chen, Bo Zheng

Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources.

4k Position

NAPGuard: Towards Detecting Naturalistic Adversarial Patches

no code implementations CVPR 2024 Siyang Wu, Jiakai Wang, Jiejie Zhao, Yazhe Wang, Xianglong Liu

For improving precision we propose the aggressive feature aligned learning to enhance the model's capability in capturing accurate aggressive patterns.

Adversarial Examples in the Physical World: A Survey

1 code implementation1 Nov 2023 Jiakai Wang, Donghua Wang, Jin Hu, Siyang Wu, Tingsong Jiang, Wen Yao, Aishan Liu, Xianglong Liu

However, current research on physical adversarial examples (PAEs) lacks a comprehensive understanding of their unique characteristics, leading to limited significance and understanding.

X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection

1 code implementation19 Feb 2023 Aishan Liu, Jun Guo, Jiakai Wang, Siyuan Liang, Renshuai Tao, Wenbo Zhou, Cong Liu, Xianglong Liu, DaCheng Tao

In this paper, we take the first step toward the study of adversarial attacks targeted at X-ray prohibited item detection, and reveal the serious threats posed by such attacks in this safety-critical scenario.

Adversarial Attack

BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance

1 code implementation13 Nov 2022 Haotong Qin, Xudong Ma, Yifu Ding, Xiaoyang Li, Yang Zhang, Zejun Ma, Jiakai Wang, Jie Luo, Xianglong Liu

We highlight that benefiting from the compact architecture and optimized hardware kernel, BiFSMNv2 can achieve an impressive 25. 1x speedup and 20. 2x storage-saving on edge hardware.

Binarization Keyword Spotting

Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection

1 code implementation23 Aug 2022 Simin Li, Huangxinxin Xu, Jiakai Wang, Aishan Liu, Fazhi He, Xianglong Liu, DaCheng Tao

The threat of fingerprint leakage from social media raises a strong desire for anonymizing shared images while maintaining image qualities, since fingerprints act as a lifelong individual biometric password.

Adversarial Attack

Defensive Patches for Robust Recognition in the Physical World

1 code implementation CVPR 2022 Jiakai Wang, Zixin Yin, Pengfei Hu, Aishan Liu, Renshuai Tao, Haotong Qin, Xianglong Liu, DaCheng Tao

For the generalization against diverse noises, we inject class-specific identifiable patterns into a confined local patch prior, so that defensive patches could preserve more recognizable features towards specific classes, leading models for better recognition under noises.

Harnessing Perceptual Adversarial Patches for Crowd Counting

1 code implementation16 Sep 2021 Shunchang Liu, Jiakai Wang, Aishan Liu, Yingwei Li, Yijie Gao, Xianglong Liu, DaCheng Tao

Crowd counting, which has been widely adopted for estimating the number of people in safety-critical scenes, is shown to be vulnerable to adversarial examples in the physical world (e. g., adversarial patches).

Crowd Counting

RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

1 code implementation11 Sep 2021 Shiyu Tang, Ruihao Gong, Yan Wang, Aishan Liu, Jiakai Wang, Xinyun Chen, Fengwei Yu, Xianglong Liu, Dawn Song, Alan Yuille, Philip H. S. Torr, DaCheng Tao

Thus, we propose RobustART, the first comprehensive Robustness investigation benchmark on ImageNet regarding ARchitecture design (49 human-designed off-the-shelf architectures and 1200+ networks from neural architecture search) and Training techniques (10+ techniques, e. g., data augmentation) towards diverse noises (adversarial, natural, and system noises).

Adversarial Robustness Benchmarking +2

Diverse Sample Generation: Pushing the Limit of Generative Data-free Quantization

1 code implementation1 Sep 2021 Haotong Qin, Yifu Ding, Xiangguo Zhang, Jiakai Wang, Xianglong Liu, Jiwen Lu

We first give a theoretical analysis that the diversity of synthetic samples is crucial for the data-free quantization, while in existing approaches, the synthetic data completely constrained by BN statistics experimentally exhibit severe homogenization at distribution and sample levels.

Data Free Quantization Image Classification

Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection

1 code implementation ICCV 2021 Renshuai Tao, Yanlu Wei, Xiangjian Jiang, Hainan Li, Haotong Qin, Jiakai Wang, Yuqing Ma, Libo Zhang, Xianglong Liu

In this work, we first present a High-quality X-ray (HiXray) security inspection image dataset, which contains 102, 928 common prohibited items of 8 categories.

A Comprehensive Evaluation Framework for Deep Model Robustness

no code implementations24 Jan 2021 Jun Guo, Wei Bao, Jiakai Wang, Yuqing Ma, Xinghai Gao, Gang Xiao, Aishan Liu, Jian Dong, Xianglong Liu, Wenjun Wu

To mitigate this problem, we establish a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i. e., data and model).

Adversarial Defense

Bias-based Universal Adversarial Patch Attack for Automatic Check-out

1 code implementation ECCV 2020 Aishan Liu, Jiakai Wang, Xianglong Liu, Bowen Cao, Chongzhi Zhang, Hang Yu

To address the problem, this paper proposes a bias-based framework to generate class-agnostic universal adversarial patches with strong generalization ability, which exploits both the perceptual and semantic bias of models.

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