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arXiv 2023年

作者： Duan, Yue Zhao, Zhen Qi, Lei Zhou, Luping Wang, Lei Shi, Yinghuan National Key Laboratory for Novel Software Technology Nanjing University China School of Electrical and Information Engineering The University of Sydney Sydney Australia School of Computer Science and Engineering Southeast University China School of Computing and Information Technology University of Wollongong Australia

While semi-supervised learning (SSL) has yielded promising results, the more realistic SSL scenario remains to be explored, in which the unlabeled data exhibits extremely high recognition difficulty, e.g., fine-grained visual classification in the context of SSL (SS-FGVC). The increased recognition difficulty on fine-grained unlabeled data spells disaster for pseudo-labeling accuracy, resulting in poor performance of the SSL model. To tackle this challenge, we propose Soft Label Selection with Confidence-Aware Clustering based on Class Transition Tracking (SoC) by reconstructing the pseudo-label selection process by jointly optimizing Expansion Objective and Shrinkage Objective, which is based on a soft label manner. Respectively, the former objective encourages soft labels to absorb more candidate classes to ensure the attendance of ground-truth class, while the latter encourages soft labels to reject more noisy classes, which is theoretically proved to be equivalent to entropy minimization. In comparisons with various state-of-the-art methods, our approach demonstrates its superior performance in SS-FGVC. Checkpoints and source code are available at https://***/NJUyued/SoC4SS-FGVC. Copyright © 2023, The Authors. All rights reserved.

关键词： Shrinkage

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Cprnc: Channels Pruning Via Reverse Neuron Crowding for Model Compression

SSRN

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SSRN 2023年

作者： Wu, Pingfan Huang, Hengyi Sun, Han Liang, Dong Liu, Ningzhong College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China

Channel pruning is an efficient technique for model compression, removing redundant parts of a convolutional neural network with minor degradation in classification accuracy. Previous criteria of channel pruning ignore neurons' intrinsic relationship and the high correlation with input samples. Inspired by the visual crowding phenomenon in neuroscience, this paper presents a novel channel pruning method via reverse neuron crowding, dubbed CPRNC, to address this issue. First, CPRNC involves a neuron crowding degree measure (NCDM) module, which builds the relationship model among all artificial neurons by observing their crowding behaviors. Subsequently, each channel's importance is evaluated by the crowding degree of corresponding channels. Considering that the channel importance is affected by the characteristic of input samples, CPRNC designs a neuron crowding degree recalibrate (NCDR) module. NCDR emphasizes discriminative samples to recalibrate the channel priority list generated by NCDM, further enhancing the precision of the pruning criterion. Experimental results show that CPRNC achieves performance that competes with state-of-the-art pruning methods, including dynamic channel pruning and learning-based pruning. For example, we prune ResNet-50 with 56.7% FLOPs on the large-scale dataset ImageNet1K with only a 0.19% decrease in accuracy. At low pruning rates, CPRNC achieves lossless compression, e.g., the pruned ResNet-56 on CIFAR-10 increases accuracy by 0.13% over the baseline model at 56.3% FLOPs reduction. © 2023, The Authors. All rights reserved.

关键词： Neurons

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ROBUST CROSS-SCENE FOREGROUND SEGMENTATION IN SURVEILLANCE VIDEO

ROBUST CROSS-SCENE FOREGROUND SEGMENTATION IN SURVEILLANCE V...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Liang, Dong Wei, Zongqi Sun, Han Zhou, Huiyu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization China School of Informatics University of Leicester United Kingdom

ISBN: (纸本)9781665438643

Training only one deep model for large-scale cross-scene video foreground segmentation is challenging due to the off-the-shelf deep learning based segmentor relies on scene-specific structural information. This results in deep models that are scene-biased and evaluations that are scene-influenced. In this paper, we integrate dual modalities (foregrounds' motion and appearance), and then eliminating features without representativeness of foreground through attention-module-guided selective-connection structures. It is in an end-to-end training manner and to achieve scene adaptation in the plug and play style. Experiments indicate the proposed method significantly outperforms the state-of-the-art deep models and background subtraction methods in untrained scenes - LIMU and LASIESTA. Source Code is available at: https://***/WeiZongqi/HOFAM. © 2021 IEEE

关键词： Optical flows

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Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration

arXiv

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arXiv 2022年

作者： Zhou, Ziqi Qi, Lei Shi, Yinghuan State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China School of Computer Science and Engineering Southeast University China

For medical image analysis, segmentation models trained on one or several domains lack generalization ability to unseen domains due to discrepancies between different data acquisition policies. We argue that the degeneration in segmentation performance is mainly attributed to overfitting to source domains and domain shift. To this end, we present a novel generalizable medical image segmentation method. To be specific, we design our approach as a multi-task paradigm by combining the segmentation model with a self-supervision domain-specific image restoration (DSIR) module for model regularization. We also design a random amplitude mixup (RAM) module, which incorporates low-level frequency information of different domain images to synthesize new images. To guide our model be resistant to domain shift, we introduce a semantic consistency loss. We demonstrate the performance of our method on two public generalizable segmentation benchmarks in medical images, which validates our method could achieve the state-of-the-art performance. ‡ Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Building Device Models Using LLMs for Requirements Simulation in Smart Homes

Building Device Models Using LLMs for Requirements Simulatio...

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IEEE International Requirements Engineering Conference Workshops (REW)

作者： Shi Chen Xiaohong Chen Zhi Jin Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Key Laboratory of High-Confidence Software Technologies Peking University Ministry of Education China Department of Computer Science and Technology Peking University Beijing China

ISBN: (数字)9798350395518

ISBN: (纸本)9798350395525

The implementation of model-based requirements simulations in smart homes is an effective strategy that allows users to comprehend the execution impact of their personalized needs before deployment. This necessitates the use of device models in advance. In this paper, we propose an automatic modeling method for smart home devices that utilizes a Large Language Model (LLM) to assist end users in modeling their devices. The method involves extracting information directly from device manuals using the extraction capabilities of the LLM, deducing the automata models using the reasoning capabilities of the LLM, and verifying the model quality through the properties of valid device models. Our evaluation has demonstrated that our method can generate high-quality device models. The resultant model can be utilized for smart home scenario simulations, assisting users in validating their requirements. Thus, this provides a cost-effective and scalable solution for offering device models to simulate user requirements in smart homes.

关键词： Large language models Conferences Automata Smart homes Manuals Information retrieval Cognition

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AIAT: Adaptive Iteration Adversarial Training for Robust Pulmonary Nodule Detection

AIAT: Adaptive Iteration Adversarial Training for Robust Pul...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Yang, Guoxing Liu, Xiaohong Shi, Jianyu Zhang, Xianchao Wang, Guangyu Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Jiaxing University Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing314001 China Peng Cheng Laboratory Department of Mathematics and Theories Shenzhen518055 China

ISBN: (纸本)9781665468190

Lung cancer is one of the leading causes of death worldwide. Early diagnosis through cancer screening can significantly improve lung cancer patients' survival. Recently, deep learning based diagnostic systems for nodule detection have shown great potential in assisting radiologists to screen cancer more efficiently. However, studies have found that deep learning models lack robustness against imperceptible crafted adversarial attacks and few studied improving the robustness of pulmonary nodule detection. Therefore, making pulmonary nodule detection models robust remains challenges. Moreover, traditional adversarial training methods either hurt the natural generalization or need expensive computational cost. To address these challenges, here we propose a novel adversarial training method called, Adaptive Iteration Adversarial Training (AIAT). AIAT generates adversarial samples by adding adversarial noise with an adaptive iteration strategy, so that it can stably and fast train models with improving robustness. Extensive experiments on the LUNA 16 dataset show that AIAT improves robustness for pulmonary nodule detection without compromising the natural generalization, and largely reduces training time. © 2022 IEEE.

关键词： Iterative methods

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Unsatisfiable Core Based Constraint Solving Cache in Symbolic Execution

Unsatisfiable Core Based Constraint Solving Cache in Symboli...

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Asia-Pacific Conference on software Engineering

作者： Ziqi Shuai Zhenbang Chen Yufeng Zhang Hengbiao Yu Ji Wang College of Computer National University of Defense Technology China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China State Key Laboratory of High Performance Computing National University of Defense Technology China College of Computer Science and Electronic Engineering Hunan University Changsha China

Constraint solving stands out as a significant bot-tleneck in symbolic execution. Caching is a commonly adopted approach to alleviate this bottleneck. However, the cutting-edge caching technique targeting unsatisfiable constraints, known as unsatisfiable core caching, primarily involves checking whether the constraint being solved contains an unsatisfiable core that has been previously collected. Such straightforward reuse frequently proves less effective in numerous scenarios. In this paper, we present a novel method to enhance the utilization of unsatisfiable cores. By excavating unsatisfiable cores, our method can compute an easily solvable over-approximation that tends to be unsatis-fiable for each constraint, which facilitates the determination of the satisfiability of the original constraint. We implemented our method on KLEE symbolic executor. The evaluation results on 27 real-world programs are encouraging.

关键词：

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UCPM: Uncertainty-Guided Cross-Modal Retrieval with Partially Mismatched Pairs

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IEEE Transactions on Image Processing 2025年 34卷 3622-3634页

作者： Zha, Quanxing Liu, Xin Cheung, Yiu-Ming Peng, Shu-Juan Xu, Xing Wang, Nannan Huaqiao University Department of Computer Science Xiamen 361021 China Key Laboratory of Pattern Recognition and Computer Vision Xiamen 361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen 361021 China Hong Kong Baptist University Department of Computer Science Hong Kong Huaqiao University Department of Artificial Intelligence Xiamen 361021 China Fujian Province University Key Laboratory of Computer Vision and Machine Learning (Huaqiao University) Xiamen 361021 China University of Electronic Science and Technology of China Center for Future Multimedia School of Computer Science and Engineering Chengdu 610051 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an 710071 China

The manual annotation of perfectly aligned labels for cross-modal retrieval (CMR) is incredibly labor-intensive. As an alternative, the collection of co-occurring data pairs from the Internet is a remarkably cost-effective way, but which, inevitably induces the Partially Mismatched Pairs (PMPs) and therefore significantly degrades the retrieval performance without particular treatment. Previous efforts often utilize the pair-wise similarity to filter out the mismatched pairs, and such operation is highly sensitive to mismatched or ambiguous data and thus leads to sub-optimal performance. To alleviate these concerns, we propose an efficient approach, termed UCPM, i.e., Uncertainty-guided Cross-modal retrieval with Partially Mismatched pairs, which can significantly reduce the adverse impact of mismatched data pairs. Specifically, a novel Uncertainty Guided Division (UGD) strategy is sophisticatedly designed to divide the corrupted training data into confident matched (clean), easily-identifiable mismatched (noisy) and hardly-determined hard subsets, and the derived uncertainty can simultaneously guide the informative pair learning while reducing the negative impact of potential mismatched pairs. Meanwhile, an effective Uncertainty Self-Correction (USC) mechanism is concurrently presented to accurately identify and rectify the fluctuated uncertainty during the training process, which further improves the stability and reliability of the estimated uncertainty. Besides, a Trusted Margin Loss (TML) is newly designed to enhance the discriminability between those hard pairs, by dynamically adjusting their soft margins to amplify the positive contributions of matched pairs while suppressing the negative impacts of mismatched pairs. Extensive experiments on three widely-used benchmark datasets, verify the effectiveness and reliability of UCPM compared with the existing SOTA approaches, and significantly improve the robustness in both synthetic and real-world PMPs. The code i

关键词： Cross-modal retrieval partially mismatched pairs trusted margin loss uncertainty guided division uncertainty self-correction

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Training Verification-Friendly Neural Networks via Neuron Behavior Consistency

arXiv

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arXiv 2024年

作者： Liu, Zongxin Zhao, Zhe Song, Fu Sun, Jun Yang, Pengfei Huang, Xiaowei Zhang, Lijun Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China RealAI Beijing China Nanjing Institute of Software Technology Nanjing China Singapore Management University Singapore College of Computer and Information Science Software College Southwest University Chongqing China The University of Liverpool Liverpool United Kingdom

Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel method for training verification-friendly neural networks, which are robust, easy to verify, and relatively accurate. Our method integrates neuron behavior consistency into the training process, making neuron activation states consistent across different inputs in a local neighborhood, reducing the number of unstable neurons and tightening the bounds of neurons thereby enhancing neural network verifiability. We evaluated our method using the MNIST, Fashion-MNIST, and CIFAR-10 datasets across various network architectures. The results of the experiment demonstrate that networks trained using our method are verification-friendly across different radii and different model architectures, whereas other tools fail to maintain verifiability as the radius increases. We also show that our method can be combined with existing methods to further improve the verifiability of networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Neurons

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Controllable Quantum Computing Privacy via Inherent Noises and Quantum Error Mitigation

Controllable Quantum Computing Privacy via Inherent Noises a...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： keyi Ju Hui Zhong Xinyue Zhang Xiaoqi Qin Miao Pan State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX Department of Computer Science Kennesaw State University Marietta GA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Quantum computing has revolutionized the approach to solving complex problems and handling vast datasets. However, data leakage in quantum computing may present privacy risks. While differential privacy (DP) has been a classical solution to protect privacy by injecting artificial noises, the implementation of DP within the quantum domain remains an under-explored area. We observe that there is a potential to leverage the inherent noises generated by Noisy Intermediate-Scale Quantum (NISQ) devices during quantum operations for achieving DP in quantum computing. Given that these inherent noises are uncontrollable, we take the lead to utilize quantum error mitigation (QEM) techniques to manage quantum differential privacy (QDP) protection levels. In our approach, we offer a tangible description of the "closeness" between neighboring quantum datasets and introduce a novel QDP definition based on observables of interest. Our simulations reveal that factors like the distance between neighboring quantum states and the number of circuit executions influence the privacy budget. Notably, by controlling the QEM samples while keeping the number of circuit executions fixed, QEM allows for substantial noise control to meet a desired privacy budget. Furthermore, we extend analysis from single-qubit to multiple qubits and discuss the impact of observables of interest on the privacy budget.

关键词： Privacy Differential privacy Quantum computing Prevention and mitigation Noise Qubit Quantum state Security Noise measurement Protection

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