Due to the dominant position of deep learning (mostly deep neural networks) in various artificial intelligence applications, recently, ensemble learning based on deep neural networks (ensemble deep learning) has shown significant performances in improving the generalization of learning system. However, since modern deep neural networks usually have millions to billions of parameters, the time and space overheads for training multiple base deep learners and testing with the ensemble deep learner are far greater than that of traditional ensemble learning. Though several algorithms of fast ensemble deep learning have been proposed to promote the deployment of ensemble deep learning in some applications, further advances still need to be made for many applications in specific fields, where the developing time and computing resources are usually restricted or the data to be processed is of large dimensionality. An urgent problem needs to be solved is how to take the significant advantages of ensemble deep learning while reduce the required expenses so that many more applications in specific fields can benefit from it. For the alleviation of this problem, it is essential to know about how ensemble learning has developed under the era of deep learning. Thus, in this article, we present fundamental discussions focusing on data analyses of published works, methodologies, recent advances and unattainability of traditional ensemble learning and ensemble deep learning. We hope this article will be helpful to realize the intrinsic problems and technical challenges faced by future developments of ensemble learning under the era of deep learning.
Geospatial Representation Learning: A Survey from Deep Learning to The LLM Era
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；The University of New South Wales（新南威尔士大学）；Southwest Jiaotong University（西南交通大学）；Institute for Infocomm Research (I$^2$R), A*STAR（信息通信研究院（I$^2$R），A*STAR）
From Time Series Analysis to Question Answering: A Survey in the LLM Era
Renmin University of China（中国人民大学）；Tsinghua University（清华大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Aalborg University（奥胡斯大学）
COOkeD: Ensemble-based OOD detection in the era of zero-shot CLIP
Aalborg University（奥尔堡大学）；ENSTA, Institut Polytechnique de Paris（法国国立高等先进技术学校，巴黎综合理工学院）；University of Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models
Salesforce AI Research（Salesforce AI研究院）；National University of Singapore（新加坡国立大学）；Squirrel Ai Learning（松鼠人工智能学习）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era
The University of Hong Kong, Hong Kong SAR（香港大学）；Nanyang Technological University, Singapore（南洋理工大学）；University of Cambridge, United Kingdom（剑桥大学）；Zhejiang Normal University, China（浙江师范大学）；The Hong Kong University of Science and Technology, Hong Kong SAR（香港科学与技术大学）；The University of Queensland（昆士兰大学）；The Hong Kong Polytechnic University, Hong Kong SAR（香港理工大学）
Ensemble Learning for Large Language Models in Text and Code Generation: A Survey
School of Computing and Engineering, University of West London（西伦敦大学计算机与工程学院）；Turing Intelligence Technology Limited（图灵智能科技有限公司）；School of Computer Science, University of Leeds（利兹大学计算机科学学院）
Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey
The University of Tokyo（东京大学）；S-Lab, Nanyang Technological University（南洋理工大学S实验室）；Duke University（杜克大学）；Salesforce AI Research（Salesforce人工智能研究）；Nanyang Technological University（南洋理工大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Tokyo University of Science（东京科学大学）
Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook
Department of Computer Science, University of Bucharest（布加勒斯特大学计算机科学系）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；Department of Computer Science, University of Central Florida（中佛罗里达大学计算机科学系）
Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era
Griffith University（格里菲斯大学）；University of Bremen（不来梅大学）；Ho Chi Minh City University of Technology（胡志明市技术大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Hanoi University of Science and Technology（河内科技大学）；The University of Queensland（昆士兰大学）
A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era
University of Bremen（不来梅大学）；Imperial College London（伦敦帝国学院）；Griffith University（格里菲斯大学）；Beijing Institute of Technology（北京理工大学）；University of Augsburg（奥格斯堡大学）；Technical University of Munich（慕尼黑工业大学）；GLAM, Imperial College London（伦敦帝国学院GLAM机构）
IP Protection in the Era of Visual Generative AI: A Survey
Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；McGill University（麦吉尔大学）；EPFL（洛桑联邦理工学院）；Université de Montréal（蒙特利尔大学）；Nanyang Technological University（南洋理工大学）；Science Tokyo（东京理科大学）；Zhejiang University（浙江大学）；Westlake University（西湖大学）；Sony Research（索尼研究院）
