Existing domain adaptation focuses on transferring knowledge between domains with categorical indices (e.g., between datasets A and B). However, many tasks involve continuously indexed domains. For example, in medical applications, one often needs to transfer disease analysis and prediction across patients of different ages, where age acts as a continuous domain index. Such tasks are challenging for prior domain adaptation methods since they ignore the underlying relation among domains. In this paper, we propose the first method for continuously indexed domain adaptation. Our approach combines traditional adversarial adaptation with a novel discriminator that models the encoding-conditioned domain index distribution. Our theoretical analysis demonstrates the value of leveraging the domain index to generate invariant features across a continuous range of domains. Our empirical results show that our approach outperforms the state-of-the-art domain adaption methods on both synthetic and real-world medical datasets.
The effectiveness of unsupervised domain adaptation degrades when there is a large discrepancy between the source and target domains. Gradual domain adaptation (GDA) is one promising way to mitigate such an issue, by leveraging additional unlabeled data that gradually shift from the source to the target. Through sequentially adapting the model along the "indexed" intermediate domains, GDA substantially improves the overall adaptation performance. In practice, however, the extra unlabeled data may not be separated into intermediate domains and indexed properly, limiting the applicability of GDA…
University of Oxford（牛津大学）；The Alan Turing Institute（艾伦·图灵研究所）
We show that normalising flows become pathological when used to model targets whose supports have complicated topologies. In this scenario, we prove that a flow must become arbitrarily numerically noninvertible in order to approximate the target closely. This result has implications for all flow-based models, and especially Residual Flows (ResFlows), which explicitly control the Lipschitz constant of the bijection used. To address this, we propose Continuously Indexed Flows (CIFs), which replace the single bijection used by normalising flows with a continuously indexed family of bijections, an…
Continuously-indexed flows (CIFs) have recently achieved improvements over baseline normalizing flows on a variety of density estimation tasks. CIFs do not possess a closed-form marginal density, and so, unlike standard flows, cannot be plugged in directly to a variational inference (VI) scheme in order to produce a more expressive family of approximate posteriors. However, we show here how CIFs can be used as part of an auxiliary VI scheme to formulate and train expressive posterior approximations in a natural way. We exploit the conditional independence structure of multi-layer CIFs to build…
Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we cast the problem of continuous adaptation into the learning-to-learn framework. We develop a simple gradient-based meta-learning algorithm suitable for adaptation in dynamically changing and adversarial scenarios. Additionally, we design a new multi-agent competitive environment, RoboSumo, and define iterated adaptation games for testing various aspects of continuous adaptation strategies. We demonstrate that meta-le…
ETH Zürich（苏黎世联邦理工学院）；MPI Informatics（马克斯·普朗克信息学研究所）；Google（谷歌）；Technical University of Munich（慕尼黑工业大学）
Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous driving systems. Existing image and video driving datasets, however, fall short of capturing the mutable nature of the real world. In this paper, we introduce the largest multi-task synthetic dataset for autonomous driving, SHIFT. It presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density. Featuring a comprehensive sensor suite and annotations for several mainstream perception tasks, SHIFT allows investigating t…
Semantic Library Adaptation: LoRA Retrieval and Fusion for Open-Vocabulary Semantic Segmentation
The Good AI Lab；University of Toronto（多伦多大学）；KTH（瑞典皇家理工学院）；AMD（超威半导体公司）；Silo AI；King；Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Google（谷歌公司）；ETH Zurich（苏黎世联邦理工学院）；University of Bologna（博洛尼亚大学）
Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks
Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Renmin University of China（中国人民大学）；the Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；the Hong Kong University of Science and Technology（香港科技大学）
Université Côte d’Azur（蔚蓝海岸大学）；Inria（法国国家信息与自动化研究所）；Centre National d’Études Spatiales（法国国家空间研究中心）；LuxCarta Technology（LuxCarta科技公司）；ACRI-ST（ACRI-ST公司）
The domain adaptation of satellite images has recently gained an increasing attention to overcome the limited generalization abilities of machine learning models when segmenting large-scale satellite images. Most of the existing approaches seek for adapting the model from one domain to another. However, such single-source and single-target setting prevents the methods from being scalable solutions, since nowadays multiple source and target domains having different data distributions are usually available. Besides, the continuous proliferation of satellite images necessitates the classifiers to…
Graz University of Technology（格拉茨技术大学）；Christian Doppler Laboratory for Embedded Machine Learning（克里斯蒂安·多普勒嵌入式机器学习实验室）
Domain adaptation is crucial to adapt a learned model to new scenarios, such as domain shifts or changing data distributions. Current approaches usually require a large amount of labeled or unlabeled data from the shifted domain. This can be a hurdle in fields which require continuous dynamic adaptation or suffer from scarcity of data, e.g. autonomous driving in challenging weather conditions. To address this problem of continuous adaptation to distribution shifts, we propose Dynamic Unsupervised Adaptation (DUA). By continuously adapting the statistics of the batch normalization layers we mod…
Télécom Paris, Institut Polytechnique de Paris（巴黎理工学院电信巴黎）；Atos（源讯公司）
Unsupervised domain adaptation for person re-identification (Person Re-ID) is the task of transferring the learned knowledge on the labeled source domain to the unlabeled target domain. Most of the recent papers that address this problem adopt an offline training setting. More precisely, the training of the Re-ID model is done assuming that we have access to the complete training target domain data set. In this paper, we argue that the target domain generally consists of a stream of data in a practical real-world application, where data is continuously increasing from the different network's c…
Contemporary domain adaptive semantic segmentation aims to address data annotation challenges by assuming that target domains are completely unannotated. However, annotating a few target samples is usually very manageable and worthwhile especially if it improves the adaptation performance substantially. This paper presents SSDAS, a Semi-Supervised Domain Adaptive image Segmentation network that employs a few labeled target samples as anchors for adaptive and progressive feature alignment between labeled source samples and unlabeled target samples. We position the few labeled target samples as …
Source-free Active Domain Adaptation for Diabetic Retinopathy Grading Based on Ultra-wide-field Fundus Image
Southwest University（西南大学）；Taiyuan University（太原学院）；Taiyuan University of Technology（太原理工大学）；Shanxi Eye Hospital（山西省眼科医院）；Nanjing University of Information Science and Technology（南京信息工程大学）
Beijing University of Posts and Telecommunications（北京邮电大学）；Peking University（北京大学）
Existing domain adaptation methods assume that domain discrepancies are caused by a few discrete attributes and variations, e.g., art, real, painting, quickdraw, etc. We argue that this is not realistic as it is implausible to define the real-world datasets using a few discrete attributes. Therefore, we propose to investigate a new problem namely the Continuous Domain Adaptation (CDA) through the lens where infinite domains are formed by continuously varying attributes. Leveraging knowledge of two labeled source domains and several observed unlabeled target domains data, the objective of CDA i…
Technological developments call for increasing perception and action capabilities of robots. Among other skills, vision systems that can adapt to any possible change in the working conditions are needed. Since these conditions are unpredictable, we need benchmarks which allow to assess the generalization and robustness capabilities of our visual recognition algorithms. In this work we focus on robotic kitting in unconstrained scenarios. As a first contribution, we present a new visual dataset for the kitting task. Differently from standard object recognition datasets, we provide images of the …
School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工联合研究技术联盟）
Analytic Continual Test-Time Adaptation for Multi-Modality Corruption
South China University of Technology（华南理工大学）；Institute of Science Tokyo（东京科学研究所）；Southern University of Science and Technology（南方科技大学）；Nanyang Technological University（南洋理工大学）
Progressive Conservative Adaptation for Evolving Target Domains
The University of Hong Kong（香港大学）；Xidian University（西安电子科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；Beihang University（北京航空航天大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Cross-Domain Attribute Alignment with CLIP: A Rehearsal-Free Approach for Class-Incremental Unsupervised Domain Adaptation
School of Computer Science and Engineering（计算机科学与工程学院）；Nanjing University of Science and Technology（南京理工大学）；School of Automation Science and Electrical Engineering（自动化科学与电气工程学院）；Beihang University（北航）；School of Automation and Intelligent Sensing（自动化与智能感知学院）；Shanghai Jiao Tong University（上海交通大学）
DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation
College of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）；School of Electronics and Computer Science, University of Southampton（南安普顿大学电子与计算机科学学院）；School of Creative Technologies, Faculty of Creative and Cultural Industries, University of Portsmouth（朴茨茅斯大学创意与文化产业学院创意技术学院）；School of Computing and Mathematic Sciences, University of Leicester（莱斯特大学计算与数学科学学院）
DOTA: Distributional Test-Time Adaptation of Vision-Language Models
State Key Laboratory of Networking and Switching Technology（网络与交换技术国家重点实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；College of Intelligence and Computing（智能与计算学院）；Tianjin University（天津大学）；School of Computer Science and Technology（计算机科学与技术学院）；Harbin Institute of Technology Shenzhen（哈尔滨工业大学深圳学院）；Institute for Infocomm Research, A*STAR（信息通信研究机构，A*STAR）；Show Lab, National University of Singapore（新加坡国立大学Show实验室）；National University of Singapore（新加坡国立大学）
School of Computer Science & Engineering, Linyi University（临沂大学计算机科学与工程学院）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；School of Electronic & Information Engineering, Suzhou University of Science and Technology（苏州科技大学电子与信息工程学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem
Conduit（Conduit公司）；Broad Institute of Harvard and MIT（哈佛与麻省理工学院 broad 研究所）；Mathematical Institute, University of Oxford（牛津大学数学研究所）；FLAIR, University of Oxford（牛津大学 FLAIR 研究所）
ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services
School of Computer Science and Technology, University of Science and Technology of China（计算机科学与技术学院，中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（苏州先进研究院，中国科学技术大学）
Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks. However, in many real world settings, it is more natural to view the problem as one of minimizing the total amount of supervision --- both the number of examples needed to learn a new task and the amount of data needed for meta-learning. Such a formulation can be studied in a sequential learning setting, where tasks are presented in sequence. When studying meta-learning in this online setting, a critical question ari…
Carnegie Mellon University（卡内基梅隆大学）；NEC Laboratories America, Inc.（美国NEC实验室）；University of Washington（华盛顿大学）
This paper studies zero-shot domain adaptation where each domain is indexed on a multi-dimensional array, and we only have data from a small subset of domains. Our goal is to produce predictors that perform well on \emph{unseen} domains. We propose a model which consists of a domain-invariant latent representation layer and a domain-specific linear prediction layer with a low-rank tensor structure. Theoretically, we present explicit sample complexity bounds to characterize the prediction error on unseen domains in terms of the number of domains with training data and the number of data per dom…
In this paper, we propose a new strategy for learning inertial robotic navigation models. The proposed strategy enhances the generalisability of end-to-end inertial modelling, and is aimed at wheeled robotic deployments. Concretely, the paper describes the following. (1) Using precision robotics, we empirically characterise the effect of changing the sensor position during navigation on the distribution of raw inertial signals, as well as the corresponding impact on learnt latent spaces. (2) We propose neural architectures and algorithms to assimilate knowledge from an indexed set of sensor po…
Insight Centre for Data Analytics（洞察数据分析中心）；Aylien Ltd.（Aylien有限公司）；National University of Ireland, Galway（爱尔兰国立大学戈尔韦分校）
Domain adaptation is crucial in many real-world applications where the distribution of the training data differs from the distribution of the test data. Previous Deep Learning-based approaches to domain adaptation need to be trained jointly on source and target domain data and are therefore unappealing in scenarios where models need to be adapted to a large number of domains or where a domain is evolving, e.g. spam detection where attackers continuously change their tactics. To fill this gap, we propose Knowledge Adaptation, an extension of Knowledge Distillation (Bucilua et al., 2006; Hinton …
