In many practical applications, it is often difficult and expensive to obtain enough large-scale labeled data to train deep neural networks to their full capability. Therefore, transferring the learned knowledge from a separate, labeled source domain to an unlabeled or sparsely labeled target domain becomes an appealing alternative. However, direct transfer often results in significant performance decay due to domain shift. Domain adaptation (DA) addresses this problem by minimizing the impact of domain shift between the source and target domains. Multi-source domain adaptation (MDA) is a powerful extension in which the labeled data may be collected from multiple sources with different distributions. Due to the success of DA methods and the prevalence of multi-source data, MDA has attracted increasing attention in both academia and industry. In this survey, we define various MDA strategies and summarize available datasets for evaluation. We also compare modern MDA methods in the deep learning era, including latent space transformation and intermediate domain generation. Finally, we discuss future research directions for MDA.
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
Domain adaptation deals with adapting behaviour of machine learning based systems trained using samples in source domain to their deployment in target domain where the statistics of samples in both domains are dissimilar. The task of directly training or adapting a learner in the target domain is challenged by lack of abundant labeled samples. In this paper we propose a technique for domain adaptation in stacked autoencoder (SAE) based deep neural networks (DNN) performed in two stages: (i) unsupervised weight adaptation using systematic dropouts in mini-batch training, (ii) supervised fine-tu…
In the current era, image manipulation is becoming increasingly easier, yielding more natural looking images, owing to the modern tools in image processing and computer vision techniques. The task of the segregation of forged images has become very challenging. To tackle such problems, publicly available datasets are insufficient. In this paper, we propose to create a synthetic forged dataset using deep semantic image inpainting algorithm. Furthermore, we use an unsupervised domain adaptation network to detect copy-move forgery in images. Our approach can be helpful in those cases, where the c…
Machine learning algorithms have revolutionized different fields, including natural language processing, computer vision, signal processing, and medical data processing. Despite the excellent capabilities of machine learning algorithms in various tasks and areas, the performance of these models mainly deteriorates when there is a shift in the test and training data distributions. This gap occurs due to the violation of the fundamental assumption that the training and test data are independent and identically distributed (i.i.d). In real-world scenarios where collecting data from all possible d…
Tsinghua University（清华大学）；Hong Kong University of Science and Technology（香港科技大学）
Domain adaptation is critical for learning in new and unseen environments. With domain adversarial training, deep networks can learn disentangled and transferable features that effectively diminish the dataset shift between the source and target domains for knowledge transfer. In the era of Big Data, the ready availability of large-scale labeled datasets has stimulated wide interest in partial domain adaptation (PDA), which transfers a recognizer from a labeled large domain to an unlabeled small domain. It extends standard domain adaptation to the scenario where target labels are only a subset…
Enhancing model prediction confidence on target data is an important objective in Unsupervised Domain Adaptation (UDA). In this paper, we explore adversarial training on penultimate activations, i.e., input features of the final linear classification layer. We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features, as used in previous works. Furthermore, with activation normalization commonly used in domain adaptation to reduce domain gap, we derive two variants and …
In few-shot domain adaptation (FDA), classifiers for the target domain are trained with accessible labeled data in the source domain (SD) and few labeled data in the target domain (TD). However, data usually contain private information in the current era, e.g., data distributed on personal phones. Thus, the private information will be leaked if we directly access data in SD to train a target-domain classifier (required by FDA methods). In this paper, to thoroughly prevent the privacy leakage in SD, we consider a very challenging problem setting, where the classifier for the TD has to be traine…
In the current era, an increasing number of machine learning models is generated for the automation of industrial processes. To that end, machine learning models are trained using historical data of each single asset leading to the development of asset-based models. To elevate machine learning models to a higher level of learning capability, domain adaptation has opened the door for extracting relevant patterns from several assets combined together. In this research we are focusing on translating the specific asset-based historical data (source domain) into data corresponding to one reference …
Deep networks have been used to learn transferable representations for domain adaptation. Existing deep domain adaptation methods systematically employ popular hand-crafted networks designed specifically for image-classification tasks, leading to sub-optimal domain adaptation performance. In this paper, we present Neural Architecture Search for Domain Adaptation (NASDA), a principle framework that leverages differentiable neural architecture search to derive the optimal network architecture for domain adaptation task. NASDA is designed with two novel training strategies: neural architecture se…
Fermi National Accelerator Laboratory（费米国家加速器实验室）；Argonne National Laboratory（阿贡国家实验室）；Kavli Institute for Cosmological Physics（卡夫利宇宙物理研究所）；University of Chicago（芝加哥大学）
In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on images from one data domain often leads to very poor performance on any other dataset. Here we develop a Universal Domain Adaptation method DeepAstroUDA, capable of performing semi-supervised domain alignment that can be applied to datasets with different types of class overlap. Extra classes can be present in any of the two datasets, and the method can even…
VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-training
University College London（伦敦大学学院）；The University of Queensland（昆士兰大学）；Southeast University（东南大学）；University of Science and Technology of China（中国科学技术大学）；Auckland University of Technology（奥克兰理工大学）
Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego（斯瓦茨计算神经科学中心，神经计算研究所，加州大学圣地亚哥分校）；Yneuro；Centre de Recherche Cerveau et Cognition, CNRS, Université Paul Sabatier（脑与认知研究中心，法国国家科学研究中心，保罗·萨巴蒂埃大学）
Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications
Renmin University of China（中国人民大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen University（深圳大学）；City University of Hong Kong（香港城市大学）；Peking University（北京大学）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）
Systematic Adaptation of Communication-focused Machine Learning Models from Real to Virtual Environments for Human-Robot Collaboration
School of Engineering（工程学院）；The University of British Columbia（不列颠哥伦比亚大学）；Department of Aerospace Engineering（航空航天工程系）；Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）；Department of Mechanical Engineering（机械工程系）；University of Victoria（维多利亚大学）
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）
Concept Drift Adaptation in Text Stream Mining Settings: A Systematic Review
Instituto Federal de Santa Catarina（圣卡塔琳娜联邦教育、科学和技术学院）；Pontifícia Universidade Católica do Paraná (PUCPR)（巴拉那天主教大学）；Instituto Federal de São Paulo（圣保罗联邦教育、科学和技术学院）；Universidade de Campinas（坎皮纳斯大学）；École de Technologie Supérieure, Université du Québec（魁北克大学高等技术学院）；Universidade Estadual de Ponta Grossa（庞塔格罗萨州立大学）
Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation
Faculty of Computer Science and Business Information Systems（计算机科学与商业信息系统系）；Technical University of Applied Sciences Würzburg-Schweinfurt（应用技术大学Würzburg-Schweinfurt）；Faculty of Technology（技术系）；Bielefeld University（比勒菲尔德大学）；Center for Artificial Intelligence（人工智能中心）；IAV GmbH
