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1. Research on Option-Critic algorithm based Representation Erasure | |||
Meng JunWei,Hu Zheng | |||
Computer Science and Technology 06 February 2024 | |||
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Abstract:The Option-Critic (OC) framework can extract transferrable abstract knowledge without requiring any environment-specific prior knowledge, learning options (a form of temporal abstract policy) end-to-end. However, the OC framework exhibits lower data efficiency in transfer tasks. During the learning process, each option considers the entire task's state space, thereby increasing the scale of policy space search. This paper proposes an Option Learning algorithm based on Representation Erasure, which introduces the Representation Erasure method to clearly quantify the influence of each dimension on high-level and low-level policy learning. It identifies and erases dimensions that significantly interfere with training, effectively reducing the scale of policy space search. Through theoretical derivation and experimental validation, this paper demonstrates the effectiveness of the Representation Erasure-based Option Learning algorithm. | |||
TO cite this article:Meng JunWei,Hu Zheng. Research on Option-Critic algorithm based Representation Erasure[OL].[ 6 February 2024] http://en.paper.edu.cn/en_releasepaper/content/4762077 |
2. End-to-end 3D Human Pose Estimation using Dual Decoders | |||
WANG Zhang,SONG Mei,JIN Lei | |||
Computer Science and Technology 02 February 2024 | |||
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Abstract:Existing methods for 3D human pose estimation mainly divide the task into two stages. The first stage identifies the 2D coordinates of the human joints in the input image, namely the 2D human joint coordinates. The second stage uses the results from the first stage as input to recover the depth information of human joints from the 2D human joint coordinates to achieve 3D human pose estimation. However, the recognition accuracy of the two-stage method relies heavily on the results of the first stage and includes too many redundant processing steps, which reduces the inference efficiency of the network. To address these issues, we propose the EDD, a fully End-to-end 3D human pose estimation method based on transformer architecture with Dual Decoders. By learning multiple human poses, the model can directly infer all 3D human poses in the image using a pose decoder, and then further optimize the recognition result using a joint decoder based on the kinematic relations between joints. With the attention mechanism, this method can adaptively focus on the most relevant features to the target joint, effectively overcoming the feature misalignment problem in the human pose estimation task and greatly improving the model performance. Any complex post-processing step, such as non-maximum suppression, is eliminated, further improving the efficiency of the model. The results show that this method achieves an accuracy of 87.4\% on the MuPoTS-3D dataset, significantly improving the accuracy of the end-to-end 3D human pose estimation method based on mixed training. | |||
TO cite this article:WANG Zhang,SONG Mei,JIN Lei. End-to-end 3D Human Pose Estimation using Dual Decoders[OL].[ 2 February 2024] http://en.paper.edu.cn/en_releasepaper/content/4761949 |
3. Research on Deep Learning Stock Selection Method Based on Trend Decomposition Algorithm | |||
Wu Cheng-Hui,Zhang Hong-Jian | |||
Computer Science and Technology 20 June 2023 | |||
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Abstract:Some studies using machine learning and deep learning models did not fully explore other indicator features that affect stock price trends, resulting in significant prediction errors and difficulty in directly applying the prediction results to obtain excess returns. Therefore, it is necessary to establish an efficient deep learning model based on stock characteristics and fully explore the factors that affect stock price trends to improve prediction accuracy.In view of the shortcomings of traditional stock trend prediction, this paper constructs LSTM-BP neural network based on Tianniuxu optimization for stock price prediction, and constructs PCA-BP neural network model based on particle swarm optimization based on multi angle correlation analysis for trend prediction. Taking into account both price prediction and trend prediction dimensions, construct a deep quantitative stock selection method. In this process, a stock trend decomposition judgment algorithm is proposed to identify and classify the stock data, improve the data utilization, build a new data set, and use the particle swarm optimization to adjust and optimize the parameters.The results of strategy backtesting show that compared to other deep learning strategies and benchmark strategies, the stock selection method and strategy proposed in this article have significantly improved in terms of victory and return. This indicates that the model has good predictive ability and application potential, and can provide effective decision support for stock investment. | |||
TO cite this article:Wu Cheng-Hui,Zhang Hong-Jian. Research on Deep Learning Stock Selection Method Based on Trend Decomposition Algorithm[OL].[20 June 2023] http://en.paper.edu.cn/en_releasepaper/content/4760906 |
4. Federated learning based on hybrid blockchain | |||
FAN Linxuan,LI Lixiang | |||
Computer Science and Technology 16 March 2023 | |||
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Abstract:With the rapid development of technologies such as machine learning, 5G communication, edge computing, artificial intelligence and blockchain, the field of machine learning has produced some new training methods. Among them, federated learning is a typical representative of distributed machine learning. Compared with traditional machine learning, federated learning can collaborate on model training between organizations without exchanging original data sets, which ensures the security and privacy of organizational data. As a kind of distributed machine learning, federated learning is faced with severe technical challenges in the process of model training: low participation in the training of edge nodes, untrustworthy edge nodes and untraceable training data. To solve the above problems, based on the federated learning theory and the blockchain theory, this paper carries out relevant research on the federated learning algorithm based on hybrid blockchain and the blockchain incentive mechanism. The main research achievements and innovations of this paper are as follows: the existing federated learning algorithm hides training data, which gives the attacker an opportunity to exploit, and the attacker can use this defect to carry out backdoor attacks on model training. In addition, in the training process of federated learning algorithm, the identity of the participating nodes is not authenticated, so that the attacker can pretend the nodes to contribute dirty data, which reduces the accuracy of model training. Therefore, this paper proposes a federated learning algorithm based on hybrid blockchain, which mainly adopts consortium blockchain to authenticate and manage the identity of nodes participating in training. Meanwhile, public blockchain is used to store training parameters to achieve traceability of training data. In addition, the introduction of blockchain architecture enables federated learning to be further decentralized. The results of simulation experiments show that the proposed scheme has advantages in robustness and accuracy of model training under the same training task. | |||
TO cite this article:FAN Linxuan,LI Lixiang. Federated learning based on hybrid blockchain[OL].[16 March 2023] http://en.paper.edu.cn/en_releasepaper/content/4759526 |
5. KDText: A Lightweight Scene Text Detector with Decoupling-Based Knowledge Distillation | |||
Lei Siyue,Yan DanFeng | |||
Computer Science and Technology 28 February 2023 | |||
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Abstract:In recent years, scene text detection task has made great progress. However, most of works are devoted to improving the performance of detector and pay little attention to the practical applications. In this paper, we propose to exploit a mask branch to detect arbitrary-shaped text accurately, and compress the model to reduce computation and storage costs, achieving a balance between speed and accuracy. Specifically, we distill intermediate features and text proposal classification to transfer dark knowledge to student text detector. In the distillation process, we treat textual and Background features differently and decouple positive and negative text proposals. Experimental results on the ICDAR 2015 and ICDAR 2017 MLT datasets demonstrate the superiority of our lightweight scene text detector. | |||
TO cite this article:Lei Siyue,Yan DanFeng. KDText: A Lightweight Scene Text Detector with Decoupling-Based Knowledge Distillation[OL].[28 February 2023] http://en.paper.edu.cn/en_releasepaper/content/4759241 |
6. RKDE: Data Compression for Time-Series Data based on Kernel Density Estimation in Reservoir Algorithm . | |||
CAO Yanyun,XU Peng | |||
Computer Science and Technology 15 February 2023 | |||
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Abstract:Edge-cloud collaborative anomaly detection has become the most important anomaly detection architecture. However, only in the most ideal state can the central cloud platform be fully trained with sufficient data. In the case of limited communication, we have to consider reducing the use of communication resources, but still maintain a high accuracy rate of anomaly detection. In this context, RKDE, a reservoir sampling algorithm based on kernel density estimation, is proposed to reduce the amount of data uploaded to the cloud by the edge end. By improving the probability of abnormal data being sampled, the compression pool of upload is constructed, the redundant process in gradient exchange is reduced, and the sampling process is timely fed back and adjusted according to the abnormal detection results in the cloud. At the same time, RKDE is compared with several sampling algorithms to demonstrate its performance advantages. | |||
TO cite this article:CAO Yanyun,XU Peng. RKDE: Data Compression for Time-Series Data based on Kernel Density Estimation in Reservoir Algorithm .[OL].[15 February 2023] http://en.paper.edu.cn/en_releasepaper/content/4759073 |
7. An Image Steganalysis Model Based on Local Difference Analysis | |||
Han Ming,Jin ZhuJun,Yang Yu | |||
Computer Science and Technology 01 February 2023 | |||
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Abstract:Current image steganalysis models usually extract global features to distinguish between natural image and steganographic image. However, the global features that represent the differences between images are closely related to image properties, such as embedding payload, which will mostly lead to severe performance deterioration if there is a mismatch between training and detecting sources. On the contrary, the local feature changes of the moving window in an image are mainly affected by data hiding, but not by the image properties. Therefore, a detection method based on local difference analysis is proposed in this paper. By analyzing the smoothness changes of local moving blocks, the impact of payload mismatch on steganalysis accuracy is reduced. In addition, this paper also introduces long short-term memory network technique into image steganalysis, and proposes a new detection model called ILDA-Net (Image Local Difference Analysis Net), which analyzes the changes in the local residual sequence to achieve steganalysis. Experiments on LSB and WOW steganography algorithms show that ILDA-Net can effectively reduce the impact of payload mismatch on network detection performance. | |||
TO cite this article:Han Ming,Jin ZhuJun,Yang Yu. An Image Steganalysis Model Based on Local Difference Analysis[OL].[ 1 February 2023] http://en.paper.edu.cn/en_releasepaper/content/4758921 |
8. Occlusion-Aware Detector: A Better Method for Occluded Pedestrian Detection | |||
Shuai Gao,Yulong Wang,,Tongcun Liu,Tong Xu,Jianxin Liao | |||
Computer Science and Technology 17 November 2022 | |||
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Abstract:To overcome the challenge of occlusion in pedestrian detection, an occlusion-aware detector (OAD) is proposed in this paper. Specifically, to deal with the dilemma problem of non-maximum suppression in solving intra-class occlusion, the crowd-counting method was designed to denote the density map of pedestrians, which can be easily applied to anchor-free models. For inter-class occlusion, we innovatively introduce contrast learning into pedestrian detection to weaken the features of occlusion and strengthen the features of visible parts. Extensive experiments were conducted on two benchmarks, including CityPersons and Caltech, and the results show that our model can achieve significant performance improvement compared with state-of-the-art approaches. | |||
TO cite this article:Shuai Gao,Yulong Wang,,Tongcun Liu, et al. Occlusion-Aware Detector: A Better Method for Occluded Pedestrian Detection[OL].[17 November 2022] http://en.paper.edu.cn/en_releasepaper/content/4758379 |
9. Opponent Modeling with Limited Cognition | |||
WANG Die,LIU Xuan,CHEN Xin-Ning,WANG Hao,ZHANG Shi-Geng | |||
Computer Science and Technology 19 May 2022 | |||
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Abstract:It is difficult for agents to learn effective policies in multi-agent systems, as the co-evolution of multiple agents leads to the non-stationarity of environments. To cope with this inherent problem of multi-agent systems, recent works have tried to stabilize policy learning by reasoning the policies of other agents, known as opponent modeling. However, existing opponent modeling methods always require agents have a fully observable of the environment, which is an unrealistic assumption. For many real-world multi-agent tasks, each agent usually has limited knowledge of the environment, especially in partially observable environments, which brings great difficulty for existing methods to learn opponent modeling. In this paper, we propose a novel opponent modeling framework POLO (Opponent Modeling with Limited Cognition) to model other agents in limited cognitive environments. POLO allows agents to reason intention and behavior with merely their own local observations using attention mechanism and behavior modeling network, thus freeing agents from the demand on fully cognitive environments and access to the opponent's observations or actions. We evaluate our framework on several partially-observable tasks and find it surpasses existing works in convergent speed and policy performance. | |||
TO cite this article:WANG Die,LIU Xuan,CHEN Xin-Ning, et al. Opponent Modeling with Limited Cognition[OL].[19 May 2022] http://en.paper.edu.cn/en_releasepaper/content/4757776 |
10. Convolutional Neural Network Based on Optical Flow for Deepfake Detection | |||
Yang Piaoyang,Gao Yuanyuan | |||
Computer Science and Technology 16 May 2022 | |||
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Abstract:With the development and popularization of communications technology, image and video play a more and more important role in the media, and the harm of image and video forgery is becoming more and more intense. Especially for the deeply forged video of human face, because the forgery effect of this kind of video is fine, it has strong deception and does great harm to the social credit system. The research on the detection of deep forged video has attracted the attention of scholars all over the world. The methods used can be divided into traditional methods and methods based on deep learning. Traditional methods have poor identification effect on fine forged videos or need manual participation. The method based on deep learning has the disadvantages of poor interpretability and insufficient generalization because it relies too much on data sets. In this paper, an identification model based on optical flow is proposed and tested on public data sets, which has achieved excellent results. The structure of the model proposed in this paper is simple, and the clues of identification basis are easier to understand. Experiments show that the model proposed in this paper has better interpretability and generalization. | |||
TO cite this article:Yang Piaoyang,Gao Yuanyuan. Convolutional Neural Network Based on Optical Flow for Deepfake Detection[OL].[16 May 2022] http://en.paper.edu.cn/en_releasepaper/content/4757774 |
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