University of Shanghai for Science and Technology（上海理工大学）；Universität Hamburg（汉堡大学）；ComOriginMat Inc.（科里金马特公司）；Chongqing University（重庆大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Surrey（萨里大学）
Song Tang, Wenxin Su, Yan Gan, Mao Ye, Jianwei Zhang, Xiatian Zhu
备注：
This paper is accepted by ICLR 2025 (Oral, Top 1.8%)
Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to an unlabeled target domain with no access to the source data. Inspired by the success of large Vision-Language (ViL) models in many applications, the latest research has validated ViL's benefit for SFDA by using their predictions as pseudo supervision. However, we observe that ViL's supervision could be noisy and inaccurate at an unknown rate, introducing additional negative effects during adaption. To address this thus-far ignored challenge, we introduce a novel Proxy Denoising (ProDe) approach. The key idea is to leverage the ViL model as a proxy to facilitate the adaptation process towards the latent domain-invariant space. We design a proxy denoising mechanism to correct ViL's predictions, grounded on a proxy confidence theory that models the dynamic effect of proxy's divergence against the domain-invariant space during adaptation. To capitalize on the corrected proxy, we derive a mutual knowledge distilling regularization. Extensive experiments show that ProDe significantly outperforms current state-of-the-art alternatives under the conventional closed set setting and more challenging open set, partial set, generalized SFDA, multi-target, multi-source, and test-time settings. Our code and data are available at https://github.com/tntek/source-free-domain-adaptation.
Recently, Neural Architecture Search (NAS) methods have been introduced and show impressive performance on many benchmarks. Among those NAS studies, Neural Architecture Transformer (NAT) aims to adapt the given neural architecture to improve performance while maintaining computational costs. However, NAT lacks reproducibility and it requires an additional architecture adaptation process before network weight training. In this paper, we propose proxyless neural architecture adaptation that is reproducible and efficient. Our method can be applied to both supervised learning and self-supervised l…
Semi-supervised domain adaptation (SSDA) adapts a learner to a new domain by effectively utilizing source domain data and a few labeled target samples. It is a practical yet under-investigated research topic. In this paper, we analyze the SSDA problem from two perspectives that have previously been overlooked, and correspondingly decompose it into two \emph{key subproblems}: \emph{robust domain adaptation (DA) learning} and \emph{maximal cross-domain data utilization}. \textbf{(i)} From a causal theoretical view, a robust DA model should distinguish the invariant ``concept'' (key clue to image…
Anhui University（安徽大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Science and Technology of China（中国科学技术大学）
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Owing to privacy concerns and heavy data transmission, source-free UDA, exploiting the pre-trained source models instead of the raw source data for target learning, has been gaining popularity in recent years. Some works attempt to recover unseen source domains with generative models, however introducing additional network parameters. Other works propose to fine-tune the source model by pseudo labels, while noisy pseudo labels may misguide the decision boundary, leading t…
Simulators can efficiently generate large amounts of labeled synthetic data with perfect supervision for hard-to-label tasks like semantic segmentation. However, they introduce a domain gap that severely hurts real-world performance. We propose to use self-supervised monocular depth estimation as a proxy task to bridge this gap and improve sim-to-real unsupervised domain adaptation (UDA). Our Geometric Unsupervised Domain Adaptation method (GUDA) learns a domain-invariant representation via a multi-task objective combining synthetic semantic supervision with real-world geometric constraints on…
Unsupervised domain adaptation (UDA) aims to train a target classifier with labeled samples from the source domain and unlabeled samples from the target domain. Classical UDA learning bounds show that target risk is upper bounded by three terms: source risk, distribution discrepancy, and combined risk. Based on the assumption that the combined risk is a small fixed value, methods based on this bound train a target classifier by only minimizing estimators of the source risk and the distribution discrepancy. However, the combined risk may increase when minimizing both estimators, which makes the…
Radboud University Medical Center（拉德堡德大学医学中心）；The Netherlands Cancer Institute（荷兰癌症研究所）；Nanjing University of Information Science and Technology（南京信息工程大学）
Domain adaptation has been widely adopted to transfer styles across multi-vendors and multi-centers, as well as to complement the missing modalities. In this challenge, we proposed an unsupervised domain adaptation framework for cross-modality vestibular schwannoma (VS) and cochlea segmentation and Koos grade prediction. We learn the shared representation from both ceT1 and hrT2 images and recover another modality from the latent representation, and we also utilize proxy tasks of VS segmentation and brain parcellation to restrict the consistency of image structures in domain adaptation. After …
Hanyang University（汉阳大学）；Chungnam National University（忠南国立大学）；SK T-Brain
Under certain statistical assumptions of noise, recent self-supervised approaches for denoising have been introduced to learn network parameters without true clean images, and these methods can restore an image by exploiting information available from the given input (i.e., internal statistics) at test time. However, self-supervised methods are not yet combined with conventional supervised denoising methods which train the denoising networks with a large number of external training samples. Thus, we propose a new denoising approach that can greatly outperform the state-of-the-art supervised de…
Korea Advanced Institute of Science and Technology（韩国科学技术院）
In recent years, a ton of research has been conducted on real image denoising tasks. However, the efforts are more focused on improving real image denoising through creating a better network architecture. We explore a different direction where we propose to improve real image denoising performance through a better learning strategy that can enable test-time adaptation on the multi-task network. The learning strategy is two stages where the first stage pre-train the network using meta-auxiliary learning to get better meta-initialization. Meanwhile, we use meta-learning for fine-tuning (meta-tra…
Shandong University（山东大学）；Nanjing University（南京大学）；Shandong University of Finance and Economics（山东财经大学）
In non-stationary environments, learning machines usually confront the domain adaptation scenario where the data distribution does change over time. Previous domain adaptation works have achieved great success in theory and practice. However, they always lose robustness in noisy environments where the labels and features of examples from the source domain become corrupted. In this paper, we report our attempt towards achieving accurate noise-robust domain adaptation. We first give a theoretical analysis that reveals how harmful noises influence unsupervised domain adaptation. To eliminate the …
We propose an extended framework for marginalized domain adaptation, aimed at addressing unsupervised, supervised and semi-supervised scenarios. We argue that the denoising principle should be extended to explicitly promote domain-invariant features as well as help the classification task. Therefore we propose to jointly learn the data auto-encoders and the target classifiers. First, in order to make the denoised features domain-invariant, we propose a domain regularization that may be either a domain prediction loss or a maximum mean discrepancy between the source and target data. The noise m…
University of California at San Diego（加利福尼亚大学圣迭戈分校）；Purdue University（普渡大学）
We propose an adaptive learning procedure to learn patch-based image priors for image denoising. The new algorithm, called the Expectation-Maximization (EM) adaptation, takes a generic prior learned from a generic external database and adapts it to the noisy image to generate a specific prior. Different from existing methods that combine internal and external statistics in ad-hoc ways, the proposed algorithm is rigorously derived from a Bayesian hyper-prior perspective. There are two contributions of this paper: First, we provide full derivation of the EM adaptation algorithm and demonstrate m…
Prototype-Based Pseudo-Label Denoising for Source-Free Domain Adaptation in Remote Sensing Semantic Segmentation
College of Computer Science, Sichuan University（四川大学计算机科学学院）；College of Computer Science and Cyber Security, Chengdu University of Technology（成都理工大学计算机科学与网络安全学院）
DP-TTA: Test-time Adaptation for Transient Electromagnetic Signal Denoising via Dictionary-driven Prior Regularization
College of Mechanical and Electrical Engineering, Chengdu University of Technology（成都理工大学机械与电子工程学院）；City University of Hong Kong, Department of Electrical Engineering（香港城市大学电子工程系）；Sichuan Provincial Natural Resources Survey and Design Group Co., Ltd.（四川省自然资源调查设计集团有限公司）；Massey University, School of Mathematical and Computational Sciences（马斯利大学数学与计算科学学院）
Training deep networks for semantic segmentation requires annotation of large amounts of data, which can be time-consuming and expensive. Unfortunately, these trained networks still generalize poorly when tested in domains not consistent with the training data. In this paper, we show that by carefully presenting a mixture of labeled source domain and proxy-labeled target domain data to a network, we can achieve state-of-the-art unsupervised domain adaptation results. With our design, the network progressively learns features specific to the target domain using annotation from only the source d…
Technion Institute of Technology（以色列理工学院）；Google Research（谷歌研究院）
Image denoising is a well studied problem with an extensive activity that has spread over several decades. Despite the many available denoising algorithms, the quest for simple, powerful and fast denoisers is still an active and vibrant topic of research. Leading classical denoising methods are typically designed to exploit the inner structure in images by modeling local overlapping patches, while operating in an unsupervised fashion. In contrast, recent newcomers to this arena are supervised and universal neural-network-based methods that bypass this modeling altogether, targeting the inferen…
We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From experiments, we demonstrate that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set. We also show that our proposed model, preceded by the denoising auto-encoder pretrainin…
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation
Harbin Institute of Technology at Shenzhen（哈尔滨工业大学（深圳））；The Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology（广东省航空航天通信与网络技术重点实验室）
Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；University of Trento（特伦托大学）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；School of Computer Science, University of Nottingham（诺丁汉大学计算机学院）
A Turn Toward Better Alignment: Few-Shot Generative Adaptation with Equivariant Feature Rotation
Hohai university（河海大学）；School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Institute for Infocomm Research (I 2 R), A*STAR（信息通信研究院（I2R），A*STAR）
HIR-ALIGN: Enhancing Hyperspectral Image Restoration via Diffusion-Based Data Generation
School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学学院）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；School of Mathematics and Statistics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University（西安交通大学数学与统计学学院和教育部智能网络与网络安全重点实验室）；Pazhou Laboratory (Huangpu), Guangzhou（广州黄埔 Pazhou 实验室）
University of Science and Technology of China（中国科学技术大学）；Microsoft Research Asia（微软亚洲研究院）
Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels are noisy and the target features are dispersed due to the discrepancy between source and target domains. In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation. In particular, we take one step further and exploit the feature distances from prototypes that provide richer information than mere prototypes. Specifically, we use…
Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising
Computation-based Science and Technology Research Center, The Cyprus Institute（计算科学与技术研究中心，塞浦路斯研究所）；Department of Geosciences, College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals（地质学系，石油工程与地质学院，国王法赫德石油与矿物大学）；Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology（智能系统系，电气工程、数学与计算机科学学院，代尔夫特理工大学）；Department of Mathematics and Statistics, Faculty of Pure and Applied Sciences, University of Cyprus（数学与统计学系，纯应用科学学院，塞浦路斯大学）；Department of Geoscience and Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology（地质科学与工程系，土木工程与地质科学学院，代尔夫特理工大学）
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction
University College London（伦敦大学学院）；Atinary Technologies（Atinary科技公司）；University of Bremen（不来梅大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Ajou University School of Medicine（亚洲大学医学院）；The Chinese University of Hong Kong（香港中文大学）
Pyramid Self-Contrastive Learning for Single-shot Test-time Ultrasound Image Denoising
Department of Electrical and Computer Engineering, The University of Hong Kong（香港大学电子与计算机工程系）；Department of Biomedical Engineering, Duke University（达特茅斯大学生物医学工程系）
JD AI Research（京东人工智能研究院）；The University of Adelaide（阿德莱德大学）
Training a semantic segmentation model requires a large amount of pixel-level annotation, hampering its application at scale. With computer graphics, we can generate almost unlimited training data with precise annotation. However,a deep model trained with synthetic data usually cannot directly generalize well to realistic images due to domain shift. It has been observed that highly confident labels for the unlabeled real images may be predicted relying on the labeled synthetic data. To tackle the unsupervised domain adaptation problem, we explore the possibilities to generate high-quality labe…
Domain adaptation typically requires to access source domain data to utilize their distribution information for domain alignment with the target data. However, in many real-world scenarios, the source data may not be accessible during the model adaptation in the target domain due to privacy issue. This paper studies the practical yet challenging source-free unsupervised domain adaptation problem, in which only an existing source model and the unlabeled target data are available for model adaptation. We present a novel denoised pseudo-labeling method for this problem, which effectively makes us…
