1 code implementation • 18 Apr 2024 • Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Borhane Blili-Hamelin, Kurt Bollacker, Rishi Bomassani, Marisa Ferrara Boston, Siméon Campos, Kal Chakra, Canyu Chen, Cody Coleman, Zacharie Delpierre Coudert, Leon Derczynski, Debojyoti Dutta, Ian Eisenberg, James Ezick, Heather Frase, Brian Fuller, Ram Gandikota, Agasthya Gangavarapu, Ananya Gangavarapu, James Gealy, Rajat Ghosh, James Goel, Usman Gohar, Sujata Goswami, Scott A. Hale, Wiebke Hutiri, Joseph Marvin Imperial, Surgan Jandial, Nick Judd, Felix Juefei-Xu, Foutse khomh, Bhavya Kailkhura, Hannah Rose Kirk, Kevin Klyman, Chris Knotz, Michael Kuchnik, Shachi H. Kumar, Chris Lengerich, Bo Li, Zeyi Liao, Eileen Peters Long, Victor Lu, Yifan Mai, Priyanka Mary Mammen, Kelvin Manyeki, Sean McGregor, Virendra Mehta, Shafee Mohammed, Emanuel Moss, Lama Nachman, Dinesh Jinenhally Naganna, Amin Nikanjam, Besmira Nushi, Luis Oala, Iftach Orr, Alicia Parrish, Cigdem Patlak, William Pietri, Forough Poursabzi-Sangdeh, Eleonora Presani, Fabrizio Puletti, Paul Röttger, Saurav Sahay, Tim Santos, Nino Scherrer, Alice Schoenauer Sebag, Patrick Schramowski, Abolfazl Shahbazi, Vin Sharma, Xudong Shen, Vamsi Sistla, Leonard Tang, Davide Testuggine, Vithursan Thangarasa, Elizabeth Anne Watkins, Rebecca Weiss, Chris Welty, Tyler Wilbers, Adina Williams, Carole-Jean Wu, Poonam Yadav, Xianjun Yang, Yi Zeng, Wenhui Zhang, Fedor Zhdanov, Jiacheng Zhu, Percy Liang, Peter Mattson, Joaquin Vanschoren
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
no code implementations • 16 Apr 2024 • Zhun Zhang, Yi Zeng, Qihe Liu, Shijie Zhou
In this paper, we seek to demystify this relationship by exploring the characteristics of adversarial perturbations within the frequency domain.
no code implementations • 19 Mar 2024 • Zhuowen Yuan, Zidi Xiong, Yi Zeng, Ning Yu, Ruoxi Jia, Dawn Song, Bo Li
The innovative use of constrained optimization and a fusion-based guardrail approach represents a significant step forward in developing more secure and reliable LLMs, setting a new standard for content moderation frameworks in the face of evolving digital threats.
no code implementations • 12 Mar 2024 • Yao Liang, Yuwei Wang, Yang Li, Yi Zeng
In response to this, inspired by the idea that the functions of the brain are shaped by its geometric structure, this paper integrates this idea into LoRA technology and proposes a new matrix transformation-based reparameterization method for efficient fine-tuning, named Matrix-Transformation based Low-Rank Adaptation (MTLoRA).
no code implementations • 12 Mar 2024 • Yi Zeng, Zhengning Wang, Yuxuan Liu, Tianjiao Zeng, Xuhang Liu, Xinglong Luo, Shuaicheng Liu, Shuyuan Zhu, Bing Zeng
Since texture details intertwine with compression artifacts in compressed dark images, detail enhancement and blocking artifacts suppression contradict each other in image space.
no code implementations • 7 Mar 2024 • Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng-Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson
Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems.
no code implementations • 29 Feb 2024 • Yi Zeng, Feifei Zhao, Yuxuan Zhao, Dongcheng Zhao, Enmeng Lu, Qian Zhang, Yuwei Wang, Hui Feng, Zhuoya Zhao, Jihang Wang, Qingqun Kong, Yinqian Sun, Yang Li, Guobin Shen, Bing Han, Yiting Dong, Wenxuan Pan, Xiang He, Aorigele Bao, Jin Wang
In this paper, we introduce a Brain-inspired and Self-based Artificial Intelligence (BriSe AI) paradigm.
no code implementations • 1 Feb 2024 • Yang Li, Yinqian Sun, Xiang He, Yiting Dong, Dongcheng Zhao, Yi Zeng
Efficient parallel computing has become a pivotal element in advancing artificial intelligence.
1 code implementation • 22 Jan 2024 • Sicheng Shen, Dongcheng Zhao, Guobin Shen, Yi Zeng
Spiking Neural Networks (SNNs), as the third generation of neural networks, have gained prominence for their biological plausibility and computational efficiency, especially in processing diverse datasets.
no code implementations • 12 Jan 2024 • Yuwei Wang, Yi Zeng
Concept learning is a fundamental aspect of human cognition and plays a critical role in mental processes such as categorization, reasoning, memory, and decision-making.
1 code implementation • 12 Jan 2024 • Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, Weiyan Shi
This paper introduces a new perspective to jailbreak LLMs as human-like communicators, to explore this overlooked intersection between everyday language interaction and AI safety.
no code implementations • 18 Dec 2023 • Jinxiang Lai, Wenlong Wu, Bin-Bin Gao, Jun Liu, Jiawei Zhan, Congchong Nie, Yi Zeng, Chengjie Wang
Image matching and object detection are two fundamental and challenging tasks, while many related applications consider them two individual tasks (i. e. task-individual).
no code implementations • 12 Dec 2023 • Guobin Shen, Dongcheng Zhao, Yiting Dong, Yang Li, Jindong Li, Kang Sun, Yi Zeng
Within the complex neuroarchitecture of the brain, astrocytes play crucial roles in development, structure, and metabolism.
no code implementations • 17 Nov 2023 • Guobin Shen, Dongcheng Zhao, Tenglong Li, Jindong Li, Yi Zeng
This paper introduces a unified perspective, illustrating that the time steps in SNNs and quantized bit-widths of activation values present analogous representations.
no code implementations • 9 Oct 2023 • Yuwei Wang, Enmeng Lu, Zizhe Ruan, Yao Liang, Yi Zeng
This paper presents Social data and knowledge collective intelligence platform for TRaining Ethical AI Models (STREAM) to address the challenge of aligning AI models with human moral values, and to provide ethics datasets and knowledge bases to help promote AI models "follow good advice as naturally as a stream follows its course".
1 code implementation • 5 Oct 2023 • Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, Peter Henderson
Optimizing large language models (LLMs) for downstream use cases often involves the customization of pre-trained LLMs through further fine-tuning.
no code implementations • 28 Sep 2023 • Jindong Li, Guobin Shen, Dongcheng Zhao, Qian Zhang, Yi Zeng
As a further step in supporting high-performance SNNs on specialized hardware, we introduce FireFly v2, an FPGA SNN accelerator that can address the issue of non-spike operation in current SOTA SNN algorithms, which presents an obstacle in the end-to-end deployment onto existing SNN hardware.
no code implementations • 18 Sep 2023 • Bing Han, Feifei Zhao, Wenxuan Pan, Zhaoya Zhao, Xianqi Li, Qingqun Kong, Yi Zeng
In this paper, we propose a brain-inspired continual learning algorithm with adaptive reorganization of neural pathways, which employs Self-Organizing Regulation networks to reorganize the single and limited Spiking Neural Network (SOR-SNN) into rich sparse neural pathways to efficiently cope with incremental tasks.
no code implementations • 11 Sep 2023 • Wenxuan Pan, Feifei Zhao, Zhuoya Zhao, Yi Zeng
This work explores brain-inspired neural architectures suitable for SNNs and also provides preliminary insights into the evolutionary mechanisms of biological neural networks in the human brain.
no code implementations • 23 Aug 2023 • Guobin Shen, Dongcheng Zhao, Yiting Dong, Yang Li, Feifei Zhao, Yi Zeng
This shift in focus from weight adjustment to mastering the intricacies of synaptic change offers a more flexible and dynamic pathway for neural networks to evolve and adapt.
no code implementations • 11 Aug 2023 • Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah, Alejandro F Frangi, Alena Buyx, Anais Emelie, Andrea Lara, Antonio R Porras, An-Wen Chan, Arcadi Navarro, Ben Glocker, Benard O Botwe, Bishesh Khanal, Brigit Beger, Carol C Wu, Celia Cintas, Curtis P Langlotz, Daniel Rueckert, Deogratias Mzurikwao, Dimitrios I Fotiadis, Doszhan Zhussupov, Enzo Ferrante, Erik Meijering, Eva Weicken, Fabio A González, Folkert W Asselbergs, Fred Prior, Gabriel P Krestin, Gary Collins, Geletaw S Tegenaw, Georgios Kaissis, Gianluca Misuraca, Gianna Tsakou, Girish Dwivedi, Haridimos Kondylakis, Harsha Jayakody, Henry C Woodruf, Hugo JWL Aerts, Ian Walsh, Ioanna Chouvarda, Irène Buvat, Islem Rekik, James Duncan, Jayashree Kalpathy-Cramer, Jihad Zahir, Jinah Park, John Mongan, Judy W Gichoya, Julia A Schnabel, Kaisar Kushibar, Katrine Riklund, Kensaku MORI, Kostas Marias, Lameck M Amugongo, Lauren A Fromont, Lena Maier-Hein, Leonor Cerdá Alberich, Leticia Rittner, Lighton Phiri, Linda Marrakchi-Kacem, Lluís Donoso-Bach, Luis Martí-Bonmatí, M Jorge Cardoso, Maciej Bobowicz, Mahsa Shabani, Manolis Tsiknakis, Maria A Zuluaga, Maria Bielikova, Marie-Christine Fritzsche, Marius George Linguraru, Markus Wenzel, Marleen de Bruijne, Martin G Tolsgaard, Marzyeh Ghassemi, Md Ashrafuzzaman, Melanie Goisauf, Mohammad Yaqub, Mohammed Ammar, Mónica Cano Abadía, Mukhtar M E Mahmoud, Mustafa Elattar, Nicola Rieke, Nikolaos Papanikolaou, Noussair Lazrak, Oliver Díaz, Olivier Salvado, Oriol Pujol, Ousmane Sall, Pamela Guevara, Peter Gordebeke, Philippe Lambin, Pieta Brown, Purang Abolmaesumi, Qi Dou, Qinghua Lu, Richard Osuala, Rose Nakasi, S Kevin Zhou, Sandy Napel, Sara Colantonio, Shadi Albarqouni, Smriti Joshi, Stacy Carter, Stefan Klein, Steffen E Petersen, Susanna Aussó, Suyash Awate, Tammy Riklin Raviv, Tessa Cook, Tinashe E M Mutsvangwa, Wendy A Rogers, Wiro J Niessen, Xènia Puig-Bosch, Yi Zeng, Yunusa G Mohammed, Yves Saint James Aquino, Zohaib Salahuddin, Martijn P A Starmans
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
1 code implementation • 9 Aug 2023 • Bing Han, Feifei Zhao, Yi Zeng, Wenxuan Pan, Guobin Shen
In addition, the overlapping shared structure helps to quickly leverage all acquired knowledge to new tasks, empowering a single network capable of supporting multiple incremental tasks (without the separate sub-network mask for each task).
1 code implementation • 4 Jun 2023 • Junyuan Hong, Yi Zeng, Shuyang Yu, Lingjuan Lyu, Ruoxi Jia, Jiayu Zhou
Data-free knowledge distillation (KD) helps transfer knowledge from a pre-trained model (known as the teacher model) to a smaller model (known as the student model) without access to the original training data used for training the teacher model.
Backdoor Defense for Data-Free Distillation with Poisoned Teachers Data-free Knowledge Distillation
no code implementations • 29 May 2023 • Zhenting Wang, Chen Chen, Yi Zeng, Lingjuan Lyu, Shiqing Ma
To overcome this problem, we first develop an alteration-free and model-agnostic origin attribution method via input reverse-engineering on image generation models, i. e., inverting the input of a particular model for a specific image.
no code implementations • 23 May 2023 • Dongcheng Zhao, Guobin Shen, Yiting Dong, Yang Li, Yi Zeng
Notably, our algorithm has achieved state-of-the-art performance on neuromorphic datasets DVS-CIFAR10 and N-Caltech101, and can achieve superior performance in the test phase with timestep T=1.
no code implementations • 19 May 2023 • Guobin Shen, Dongcheng Zhao, Yiting Dong, Yang Li, Yi Zeng
The biological neural network is a vast and diverse structure with high neural heterogeneity.
no code implementations • 17 May 2023 • Linghao Feng, Dongcheng Zhao, Yi Zeng
As it stands, such models are primarily limited to the domain of artificial neural networks.
1 code implementation • 28 Apr 2023 • Hoang Anh Just, Feiyang Kang, Jiachen T. Wang, Yi Zeng, Myeongseob Ko, Ming Jin, Ruoxi Jia
(1) We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between training and validation sets.
no code implementations • 21 Apr 2023 • Wenxuan Pan, Feifei Zhao, Guobin Shen, Yi Zeng
The neural motifs topology, modular regional structure and global cross-brain region connection of the human brain are the product of natural evolution and can serve as a perfect reference for designing brain-inspired SNN architecture.
no code implementations • 13 Apr 2023 • Yiting Dong, Dongcheng Zhao, Yi Zeng
However, SNNs typically grapple with challenges such as extended time steps, low temporal information utilization, and the requirement for consistent time step between testing and training.
no code implementations • 31 Mar 2023 • Wenxuan Pan, Feifei Zhao, Yi Zeng, Bing Han
For structural evolution, an adaptive evolvable LSM model is developed to optimize the neural architecture design of liquid layer with separation property.
no code implementations • 23 Mar 2023 • Xiang He, Yang Li, Dongcheng Zhao, Qingqun Kong, Yi Zeng
The self-adaptation to membrane potential and input allows a timely adjustment of the threshold to fire spike faster and transmit more information.
1 code implementation • 23 Mar 2023 • Xiang He, Dongcheng Zhao, Yang Li, Guobin Shen, Qingqun Kong, Yi Zeng
In order to improve the generalization ability of SNNs on event-based datasets, we use static images to assist SNN training on event data.
no code implementations • 22 Mar 2023 • Yuxuan Zhao, Enmeng Lu, Yi Zeng
Despite the conceptual descriptions of the mechanisms of bodily self-consciousness and the possible relevant brain areas, the existing theoretical models still lack an explanation of the computational mechanisms by which the brain encodes the perception of one's body and how our subjectively perceived body illusions can be generated by neural networks.
1 code implementation • 22 Feb 2023 • Minzhou Pan, Yi Zeng, Lingjuan Lyu, Xue Lin, Ruoxi Jia
However, we lack a thorough understanding of the applicability of existing detection methods across a variety of learning settings.
no code implementations • 29 Jan 2023 • Guobin Shen, Dongcheng Zhao, Yi Zeng
Inspired by spike patterns in biological neurons, this paper introduces the dynamic Burst pattern and designs the Leaky Integrate and Fire or Burst (LIFB) neuron that can make a trade-off between short-time performance and dynamic temporal performance from the perspective of network information capacity.
no code implementations • 18 Jan 2023 • Yinqian Sun, Yi Zeng, Feifei Zhao, Zhuoya Zhao
In this paper, we proposed a brain-inspired SNN-based deep distributional reinforcement learning algorithm with combination of bio-inspired multi-compartment neuron (MCN) model and population coding method.
no code implementations • 7 Jan 2023 • Yao Liang, Hongjian Fang, Yi Zeng, Feifei Zhao
Reasoning and question answering as a basic cognitive function for humans, is nevertheless a great challenge for current artificial intelligence.
no code implementations • 5 Jan 2023 • Jindong Li, Guobin Shen, Dongcheng Zhao, Qian Zhang, Yi Zeng
To improve memory efficiency, we design a memory system to enable efficient synaptic weights and membrane voltage memory access with reasonable on-chip RAM consumption.
no code implementations • 23 Nov 2022 • Bing Han, Feifei Zhao, Yi Zeng, Guobin Shen
The proposed DPAP model considers multiple biologically realistic mechanisms (such as dendritic spine dynamic plasticity, activity-dependent neural spiking trace, and local synaptic plasticity), with the addition of an adaptive pruning strategy, so that the network structure can be dynamically optimized during learning without any pre-training and retraining.
no code implementations • 22 Nov 2022 • Bing Han, Feifei Zhao, Yi Zeng, Wenxuan Pan
Experimental results on spatial (MNIST, CIFAR-10) and temporal neuromorphic (N-MNIST, DVS-Gesture) datasets demonstrate that our method can flexibly learn appropriate compression rate for various tasks and effectively achieve superior performance while massively reducing the network energy consumption.
1 code implementation • 2 Nov 2022 • Jinxiang Lai, Siqian Yang, Wenlong Liu, Yi Zeng, Zhongyi Huang, Wenlong Wu, Jun Liu, Bin-Bin Gao, Chengjie Wang
Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples.
1 code implementation • 12 Oct 2022 • Yi Zeng, Minzhou Pan, Himanshu Jahagirdar, Ming Jin, Lingjuan Lyu, Ruoxi Jia
Most poisoning defenses presume access to a set of clean data (or base set).
no code implementations • 8 Aug 2022 • Jinyu Fan, Yi Zeng
Even the state-of-the-art deep learning models lack fundamental abilities compared to humans.
no code implementations • 18 Jul 2022 • Yi Zeng, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yiting Dong, Enmeng Lu, Qian Zhang, Yinqian Sun, Qian Liang, Yuxuan Zhao, Zhuoya Zhao, Hongjian Fang, Yuwei Wang, Yang Li, Xin Liu, Chengcheng Du, Qingqun Kong, Zizhe Ruan, Weida Bi
These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired cognitive functions.
no code implementations • 11 Jul 2022 • Hongjian Fang, Yi Zeng, Jianbo Tang, Yuwei Wang, Yao Liang, Xin Liu
For the fields of neuroscience and cognitive science, the work in this paper provided the foundation of computational modeling for further exploration of the way the human brain represents commonsense knowledge.
no code implementations • 6 Jul 2022 • Yiting Dong, Dongcheng Zhao, Yang Li, Yi Zeng
By integrating the above three adaptive mechanisms and STB-STDP, our model greatly accelerates the training of unsupervised spiking neural networks and improves the performance of unsupervised SNNs on complex tasks.
no code implementations • 6 Jul 2022 • Yang Li, Xiang He, Yiting Dong, Qingqun Kong, Yi Zeng
Spiking neural network (SNN) has been attached to great importance due to the properties of high biological plausibility and low energy consumption on neuromorphic hardware.
no code implementations • 14 Jun 2022 • Si Chen, Yi Zeng, Jiachen T. Wang, Won Park, Xun Chen, Lingjuan Lyu, Zhuoqing Mao, Ruoxi Jia
Our work is the first to provide a thorough understanding of leveraging model inversion for effective backdoor removal by addressing key questions about reconstructed samples' properties, perceptual similarity, and the potential presence of backdoor triggers.
no code implementations • 8 Jun 2022 • Yinqian Sun, Yi Zeng, Yang Li
Brain inspired spiking neural networks (SNNs) have been successfully applied to many pattern recognition domains.
no code implementations • 24 May 2022 • Jihang Wang, Dongcheng Zhao, Guobin Shen, Qian Zhang, Yi Zeng
Privacy protection is a crucial issue in machine learning algorithms, and the current privacy protection is combined with traditional artificial neural networks based on real values.
no code implementations • 24 May 2022 • Guobin Shen, Dongcheng Zhao, Yi Zeng
Data augmentation can improve the quantity and quality of the original data by processing more representations from the original data.
1 code implementation • 28 Apr 2022 • Yang Li, Yi Zeng
Spiking neural network (SNN), as a brain-inspired energy-efficient neural network, has attracted the interest of researchers.
2 code implementations • 11 Apr 2022 • Yi Zeng, Minzhou Pan, Hoang Anh Just, Lingjuan Lyu, Meikang Qiu, Ruoxi Jia
With poisoning equal to or less than 0. 5% of the target-class data and 0. 05% of the training set, we can train a model to classify test examples from arbitrary classes into the target class when the examples are patched with a backdoor trigger.
Ranked #1 on Clean-label Backdoor Attack (0.05%) on Tiny ImageNet
no code implementations • 18 Mar 2022 • Jun Quan, Ze Wei, Qiang Gan, Jingqi Yao, Jingyi Lu, Yuchen Dong, Yiming Liu, Yi Zeng, Chao Zhang, Yongzhi Li, Huang Hu, Yingying He, Yang Yang, Daxin Jiang
The conversational recommender systems (CRSs) have received extensive attention in recent years.
1 code implementation • 25 Dec 2021 • Yang Li, Yiting Dong, Dongcheng Zhao, Yi Zeng
Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain.
no code implementations • 15 Nov 2021 • Dongcheng Zhao, Yang Li, Yi Zeng, Jihang Wang, Qian Zhang
Our Spiking CapsNet fully combines the strengthens of SNN and CapsNet, and shows strong robustness to noise and affine transformation.
no code implementations • 17 Oct 2021 • Guobin Shen, Dongcheng Zhao, Yi Zeng
Secondly, we propose a biologically plausible temporal adjustment making the error propagate across the spikes in the temporal dimension, which overcomes the problem of the temporal dependency within a single spike period of the traditional spiking neurons.
3 code implementations • ICLR 2022 • Yi Zeng, Si Chen, Won Park, Z. Morley Mao, Ming Jin, Ruoxi Jia
Particularly, its performance is more robust to the variation on triggers, attack settings, poison ratio, and clean data size.
no code implementations • 29 Sep 2021 • Tianhao Wang, Yi Zeng, Ming Jin, Ruoxi Jia
In this paper, we focus on the problem of identifying bad training data when the underlying cause is unknown in advance.
no code implementations • 10 Jun 2021 • Tianhao Wang, Yi Zeng, Ming Jin, Ruoxi Jia
High-quality data is critical to train performant Machine Learning (ML) models, highlighting the importance of Data Quality Management (DQM).
no code implementations • 27 May 2021 • Yang Li, Yi Zeng, Dongcheng Zhao
Also, when ResNet structure-based ANNs are converted, the information of output neurons is incomplete due to the rapid transmission of the shortcut path.
no code implementations • 27 May 2021 • Dongcheng Zhao, Yi Zeng, Yang Li
With the combination of the two mechanisms, we propose a deep spiking neural network with adaptive self-feedback and balanced excitatory and inhibitory neurons (BackEISNN).
1 code implementation • ICCV 2021 • Yi Zeng, Won Park, Z. Morley Mao, Ruoxi Jia
Acknowledging previous attacks' weaknesses, we propose a practical way to create smooth backdoor triggers without high-frequency artifacts and study their detectability.
1 code implementation • 27 Mar 2021 • Yiqun Liu, Yi Zeng, Jian Pu, Hongming Shan, Peiyang He, Junping Zhang
In this work, we propose a self-supervised gait recognition method, termed SelfGait, which takes advantage of the massive, diverse, unlabeled gait data as a pre-training process to improve the representation abilities of spatiotemporal backbones.
no code implementations • 13 Dec 2020 • Han Qiu, Yi Zeng, Shangwei Guo, Tianwei Zhang, Meikang Qiu, Bhavani Thuraisingham
In this paper, we investigate the effectiveness of data augmentation techniques in mitigating backdoor attacks and enhancing DL models' robustness.
1 code implementation • 3 Dec 2020 • Han Qiu, Yi Zeng, Tianwei Zhang, Yong Jiang, Meikang Qiu
With more and more advanced adversarial attack methods have been developed, a quantity of corresponding defense solutions were designed to enhance the robustness of DNN models.
no code implementations • 23 Oct 2020 • Yinqian Sun, Yi Zeng, Tielin Zhang
Despite advances in artificial intelligence models, neural networks still cannot achieve human performance, partly due to differences in how information is encoded and processed compared to human brain.
no code implementations • 18 Sep 2020 • Shangwei Guo, Tianwei Zhang, Han Qiu, Yi Zeng, Tao Xiang, Yang Liu
In this paper, we propose a novel watermark removal attack from a different perspective.
no code implementations • 30 Jul 2020 • Yi Zeng, Han Qiu, Gerard Memmi, Meikang Qiu
Deep Neural Networks (DNNs) in Computer Vision (CV) are well-known to be vulnerable to Adversarial Examples (AEs), namely imperceptible perturbations added maliciously to cause wrong classification results.
no code implementations • 7 Jun 2020 • Qingdong He, Zhengning Wang, Hao Zeng, Yi Zeng, Yijun Liu
Accurate 3D object detection from point clouds has become a crucial component in autonomous driving.
Ranked #1 on 3D Object Detection on KITTI Pedestrians Hard
1 code implementation • 27 May 2020 • Han Qiu, Yi Zeng, Qinkai Zheng, Tianwei Zhang, Meikang Qiu, Gerard Memmi
Extensive evaluations indicate that our solutions can effectively mitigate all existing standard and advanced attack techniques, and beat 11 state-of-the-art defense solutions published in top-tier conferences over the past 2 years.
no code implementations • 19 Sep 2019 • Yi Zeng, Enmeng Lu, Yinqian Sun, Ruochen Tian
Facial recognition is changing the way we live in and interact with our society.
no code implementations • 26 Aug 2019 • Yi Zeng, Zihao Qi, Wen-Cheng Chen, Yanzhe Huang, Xingxin Zheng, Han Qiu
With more encrypted network traffic gets involved in the Internet, how to effectively identify network traffic has become a top priority in the field.
1 code implementation • ICCV 2019 • Yi Zeng, Pingping Zhang, Jianming Zhang, Zhe Lin, Huchuan Lu
This paper pushes forward high-resolution saliency detection, and contributes a new dataset, named High-Resolution Salient Object Detection (HRSOD).
Ranked #11 on RGB Salient Object Detection on DAVIS-S (using extra training data)
no code implementations • 14 Aug 2019 • Hongyin Zhu, Wenpeng Hu, Yi Zeng
Named entity recognition (NER) is a foundational technology for information extraction.
no code implementations • 12 Dec 2018 • Yi Zeng, Enmeng Lu, Cunqing Huangfu
Artificial Intelligence principles define social and ethical considerations to develop future AI.