Progressive Learning Algorithm for Efficient Person Re- Identification

Li, Zhen; Shao, Hanyang; Niu, Liang; Xue, Nian

doi:10.1109/icpr48806.2021.9413306

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…After the deep neural network (also called deep learning) [17] became popular in various areas, such as machine translation [2,41], chatbot [42], unmanned aerial vehicle [43], person re-identification [44,45], multiple object tracking [46], image recognition [47,48] and signal processing [49], explainable AI received attention again. This time, people saw the power of deep learning and never doubted its ability.…”

Section: Deep Learningmentioning

confidence: 99%

Dual-Track Lifelong Machine Learning-Based Fine-Grained Product Quality Analysis

et al. 2023

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Artificial intelligence (AI) systems are becoming wiser, even surpassing human performances in some fields, such as image classification, chess, and Go. However, most high-performance AI systems, such as deep learning models, are black boxes (i.e., only system inputs and outputs are visible, but the internal mechanisms are unknown) and, thus, are notably challenging to understand. Thereby a system with better explainability is needed to help humans understand AI. This paper proposes a dual-track AI approach that uses reinforcement learning to supplement fine-grained deep learning-based sentiment classification. Through lifelong machine learning, the dual-track approach can gradually become wiser and realize high performance (while keeping outstanding explainability). The extensive experimental results show that the proposed dual-track approach can provide reasonable fine-grained sentiment analyses to product reviews and remarkably achieve a 133% promotion of the Macro-F1 score on the Twitter sentiment classification task and a 27.12% promotion of the Macro-F1 score on an Amazon iPhone 11 sentiment classification task, respectively.

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Section: Deep Learningmentioning

confidence: 99%

Dual-Track Lifelong Machine Learning-Based Fine-Grained Product Quality Analysis

et al. 2023

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“…In order to further improve the performance of the spectrum sensing model under low SNR, and to extract the time-frequency information of the received signal more effectively, this paper utilizes short-time Fourier transform to preprocess the signal. Two-dimensional gray images are generated by the time-frequency analysis of the received one-dimensional signals, which can fully reflect the frequency domain characteristics of the signal, and are supposed to exhibit strong robustness and noise immunity [25].…”

Section: Short-time Fourier Transformmentioning

confidence: 99%

Spectrum Sensing Based on STFT-ImpResNet for Cognitive Radio

2022

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Spectrum sensing is a crucial technology for cognitive radio. The existing spectrum sensing methods generally suffer from certain problems, such as insufficient signal feature representation, low sensing efficiency, high sensibility to noise uncertainty, and drastic degradation in deep networks. In view of these challenges, we propose a spectrum sensing method based on short-time Fourier transform and improved residual network (STFT-ImpResNet) in this work. Specifically, in STFT, the received signal is transformed into a two-dimensional time-frequency matrix which is normalized to a gray image as the input of the network. An improved residual network is designed to classify the signal samples, and a dropout layer is added to the residual block to mitigate over-fitting effectively. We conducted comprehensive evaluations on the proposed spectrum sensing method, which demonstrate that—compared with other current spectrum sensing algorithms—STFT-ImpResNet exhibits higher accuracy and lower computational complexity, as well as strong robustness to noise uncertainty, and it can meet the needs of real-time detection.

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“…A classification loss is an early choice [19,20], and contrastive loss is an alternative as well [21]. Many works [19,20,22] consider a multi-task objective functions to update the CNN weights. These approaches train a deep CNN…”

Section: G Loss Functionmentioning

confidence: 99%

Spectrum Sensing Method Based on Residual Cellular Network

Gai

Xue

et al. 2022

IEEE Access

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The traditional spectrum sensing method based on convolutional neural network (CNN) has the single-branch convolutional network structure and the shallow network structure which limits the ability of extracting the Primary User (PU) feature. Aiming at these problems, a spectrum sensing method based on the residual cellular network (ResCelNet) is proposed in this work, involving a structure of "dual-branch convolution plus summation operation plus residual learning plus dual-branch convolution plus summation operation". Specifically, the dual-branch convolution improves the feature extraction ability, the addition operation enhances the micro-feature information, and the residual learning is adopted to facilitate training the deep spectrum sensing network. This method transforms the spectrum sensing problem into the image binary classification problem. Firstly, the received signals are reshaped into a matrix and normalized to gray levels, which is used as the input of the network. Secondly, the feature information of gray-scale images is extracted and the network is trained through dual-branch convolution and residual learning. Finally, the test data is input into the trained model and spectrum sensing based on image classification is implemented. The experimental results demonstrate that the proposed method exhibits a higher detection probability and a lower false alarm probability as well as better generalization ability as compared with the traditional methods under low signal to noise ratio (SNR) circumstance.INDEX TERMS Spectrum sensing, residual cellular network, dual-branch convolution, residual learning.

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Progressive Learning Algorithm for Efficient Person Re- Identification

Cited by 6 publications

References 16 publications

Dual-Track Lifelong Machine Learning-Based Fine-Grained Product Quality Analysis

Dual-Track Lifelong Machine Learning-Based Fine-Grained Product Quality Analysis

Spectrum Sensing Based on STFT-ImpResNet for Cognitive Radio

Spectrum Sensing Method Based on Residual Cellular Network

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