面向變化場景的連續人工智能 李林燕 胡伏原 呂凡 9787567250154 【台灣高等教育出版社】

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書名：面向變化場景的連續人工智能
ISBN：9787567250154
出版社：蘇州大學
著編譯者：李林燕 胡伏原 呂凡
頁數：180
所在地：中國大陸 *此為代購商品
書號：1721050
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內容簡介

在人工智能飛速發展的當下，《面向變化場景的連續人工智能（英文版）》脫穎而出。它聚焦于複雜多變的現實場景，為連續人工智能的研究提供了全新視角。書中深入剖析前沿理論與算法，展示如何讓人工智能系統在動態環境中持續學習與進化，打破傳統AI局限。無論是科研人員尋求新的研究方向，還是專業人士渴望提升AI應用能力，都能從書中獲取寶貴洞見，引領讀者在人工智能的前沿領域探索，是人工智能領域極具價值的學術著作。

作者簡介

李林燕是中國蘇州蘇州經貿職業技術學院的副教授。她於2007年在中國武漢的武漢大學獲得碩士學位。她目前的研究興趣包括機器學習、神經信息處理和模式識別。

目錄

Chapter 1 Introduction
1 1 Static and Dynamic Artificial Intelligence
1 2 Theory of Continual Learning
1 2 1 Scenarios of Continual Learning
1 2 2 Challenges of Continual Learning
1 2 3 Approaches of Continual Learning
1 3 Content of This Book
Chapter 2 Multi-Domain Multi-Task Rehearsal for Continual Learning
2 1 Introduction
2 2 Methodology
2 2 1 Multi-Domain Multi-Task Rehearsal
2 2 2 Two-Level Angular Margin Loss
2 2 3 Episodic Distillation
2 2 4 Total Algorithm
2 3 Experiments
2 3 1 Experimental Settings
2 3 2 Comparison with the State-of-the-arts
2 3 3 Domain Shift Observation
2 4 Chapter Conclusion
Chapter 3 Exploring Example Influence in Continual Learning
3 1 Introduction
3 2 Methodology
3 2 1 Preliminary: Rehearsal-based CL
3 2 2 Example Influence on Stability and Plasticity
3 3 Meta Learning on Stability and Plasticity
3 3 1 Influence Function for SP
3 3 2 Simulating IF for SP
3 4 Using Influence for Continual Learning
3 4 1 Before Using: Influence for SP Pareto Optimality
3 4 2 Model Update Using Example Influence
3 4 3 Rehearsal Selection Using Example Influence
3 5 Experiments
3 5 1 Datasets and Implementation Details
3 5 2 Main Comparison Results
3 5 3 Analysis of Dataset Influence on SP
3 5 4 Analysis on SP Pareto Optimum
3 5 5 Training Time
3 6 Chapter Conclusion
Chapter 4 Measuring Asymmetric Gradient Discrepancy in Parallel Continual Learning
4 1 Introduction
4 2 Methodology
4 2 1 Parallel Continual Learning
4 2 2 Measuring Asymmetric Gradient Discrepancy
4 2 3 Maximum Discrepancy Optimization
4 3 Experiments
4 3 1 Dataset
4 3 2 Experiment Details
4 3 3 Main Results
4 3 4 Rehearsal Analysis in PCL
4 3 5 Comparison with Symmetric Metrics
4 3 6 Ablation Study
4 3 7 Procedure Time
4 3 8 Tolerance Analysis in AGD
4 4 Chapter Conclusion
Chapter 5 Multi-Label Continual Learning Using Augmented Graph Convolutional Network
5 1 Introduction
5 2 Methodology
5 2 1 Definition of MLCL
5 2 2 MLCL Scenarios
5 2 3 Overview of the Proposed Method
5 2 4 Partial Label Encoder
5 2 5 Augmented Correlation Matrix
5 2 6 Objective Function
5 3 Experiments
5 3 1 Datasets
5 3 2 Evaluation Metrics
5 3 3 Implementation Details
5 3 4 Baseline Methods
5 3 5 Main Results
5 3 6 More MLCL Settings
5 3 7 mAP Curves
5 3 8 Ablation Studies
5 3 9 Visualization of ACM
5 4 Chapter Conclusion
Chapter 6 Towards Long-Term Remembering for Federated Continual Learning
6 1 Introduction
6 1 1 Federated Learning
6 1 2 Federated Continual Learning
6 2 Methodology
6 2 1 Problem Definition
6 2 2 Multi-Node Collaborative Integration for Parameter Co-Importance
6 2 3 Fisher Accumulating and Balancing for Reducing Forgetting
6 3 Experiments
6 3 1 Experiment Details
6 3 2 Results
6 3 3 Ablation Experiment
6 3 4 Fisher Visualization
6 4 Chapter Conclusion
Chapter 7 Centroid-based Rehearsal in Online Continual Learning
7 1 Introduction
7 2 Methodology
7 2 1 Continual Domain Shift in OCL
7 2 2 Centroid-based Rehearsal
7 2 3 Distillation on Centroid Distance
7 2 4 The Overall Algorithm
7 3 Experiments
7 3 1 Dataset and Experimental Details
7 3 2 Experimental Results
7 4 Chapter Conclusion
Chapter 8 Dynamic V2X Perception from Road-to-Vehicle Vision
8 1 Introduction
8 2 Methodology
8 2 1 Overview
8 2 2 Overcoming Intra-Scene Changes
8 2 3 Overcoming Inter-Scene Changes
8 2 4 The Whole Algorithm
8 2 5 Bandwidth Discussion
8 3 Experiments
8 3 1 Data Preparation
8 3 2 Evaluation Metric
8 3 3 Compared Methods
8 3 4
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