[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82913-en":3,"doc-seo-82913-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82913,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","FlatManifold Robust Continual Learning under Severe Label Noise and Domain Shifts via Intrinsic Manifold Flattening","Non-stationary streaming environments require continual learning that adapts to complex domain shifts while resisting catastrophic degradation from severe, uncalibrated label noise. The paper introduces FlatManifold, a robust and streamlined framework that uses a Nyström manifold flattening map, kernel trick projections, and optimization in an orthogonalized RKHS. Feature distributions are mapped to a fixed orthogonal topology with ridge regularization, smoothing extreme noise effects. Catastrophic forgetting is controlled with a continual topology brake term based on past covariance. Evaluations on multi-session robotics data show strong generalization under 40% symmetric label noise and severe cross-session domain shifts, outperforming standard baselines.","FlatManifold: Robust Continual Learning under Severe Label Noise and Domain Shifts via Intrinsic Manifold Flattening  \nRai Hisada and Kanji Tanaka  \narXiv :2607 .0520 1v 1 [ cs .LG] 6 Jul 2026  \nAbstract—In non-stationary streaming environments, simultaneously adapting to complex, non-linear domain shifts via continual learning while mitigating the catastrophic effects of severe, uncalibrated label noise poses a fundamental mathematical challenge. In this paper, we propose FlatManifold, a novel, streamlined robust continual learning framework that utilizes a Nystrm manifold flattening map based on the kernel trick and projection onto an orthogonalized Reproducing Kernel Hilbert Space (RKHS).  \nUnlike traditional methods that rely on complex, error-prone sample-filtering pipelines, the proposed approach exploits the intrinsic mathematical robustness of the flattened space itself. By mapping feature distributions onto a fixed orthogonal target topology with a ridge regularizer, the framework naturally smoothes and counteracts the influence of extreme label noise during the optimization process. Concurrently, catastrophic forgetting is prevented via a continual topology brake term that leverages the covariance matrix of past experiences.  \nExtensive evaluation on real-world multi-session robotics datasets demonstrates that even under severe conditions featuring 40% symmetric label noise, FlatManifold successfully mitigates gradient corruption. Under extreme cross-session domain shifts spanning various seasons and lighting conditions, the proposed framework establishes high generalization capabilities, significantly outperforming standard sequential optimization baselines and proving that structural linearization itself serves as a powerful mathematical barrier against distributed label corruption.  \nIndex Terms—Visual Place Recognition, Lifelong Continual Learning, Label Noise, Manifold Flattening, Core Ablation Study.  \nI. INTRODUCTION  \nFor autonomous mobile robots to operate successfully in real-world environments over extended periods, the capability of lifelong continual learning is indispensable. Robots must continuously adapt to dynamic domain shifts, such as variations in weather, lighting conditions, seasons, and structural changes over time. Within the context of autonomous navigation, Visual Place Recognition (VPR) serves as a cornerstone for self-localization and loop-closure detection in Simultaneous Localization and Mapping (SLAM) frameworks. Consequently, maintaining extreme geometric and semantic robustness against environmental transitions is a critical requirement for any deployable VPR engine.  \nHowever, a fundamental assumption in conventional VPR systems is that the stream of training data encountered over a robot’s lifespan is perfectly annotated. In real-world lifelong deployments, this assumption rarely holds. Training sequences collected incrementally are invariably corrupted by  \nAll authors are with the Department of Mechanical Engineering, Faculty of Engineering, University of Fukui, Fukui 910-8507, Japan. (E-mail: tnkknj@u[fukui.ac.jp](fukui.ac.jp)).  \na substantial amount of annotation errors, commonly referred to as label noise. These errors stem from human annotator oversight, cumulative odometry drifts, and false-positive loop-closure assertions by automated SLAM front-ends. Standard continual learning algorithms, such as regularized or expansion-based networks, are predominantly engineered under the strict premise of clean training labels.  \nWhen subjected to severe label noise, these networks undergo rapid overfitting to corrupted samples. As a consequence, the parameter space is distorted, drastically accelerating catastrophic forgetting—the phenomenon where the model overwrites previously consolidated topological memories of clean, historical environments.  \nFurthermore, when a robot transitions sequentially into entirely unknown environments (e.g., traveling through a series of distinct citie","cbCaifVjK1z6NG6Q","https://ap.wps.com/l/cbCaifVjK1z6NG6Q","pdf",229063,3,1,6,"English","en",105,"# Abstract\n# Introduction\n# Related Work\n## Visual Place Recognition","[{\"question\":\"What problem does FlatManifold address in lifelong continual learning?\",\"answer\":\"It addresses simultaneous adaptation to nonlinear domain shifts in streaming settings while mitigating catastrophic effects caused by severe, uncalibrated label noise in training data.\"},{\"question\":\"How does FlatManifold achieve robustness to label noise?\",\"answer\":\"It projects high-dimensional descriptors into a flattened structure using a Nyström kernel mapper and performs learning in an orthogonalized RKHS, where the flattened space acts as a smoothing barrier that diffuses corrupted gradients.\"},{\"question\":\"How is catastrophic forgetting prevented during continual updates?\",\"answer\":\"A continual topology brake term leverages the covariance matrix of past experiences to constrain the evolution of the learned topology and preserve previously consolidated knowledge.\"}]",1784183911,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"flatmanifold-robust-continual-learning-under-severe-label-noise-and-domain-shifts-via-intrinsic-manifold-flattening","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/flatmanifold-robust-continual-learning-under-severe-label-noise-and-domain-shifts-via-intrinsic-manifold-flattening/82913/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does FlatManifold address in lifelong continual learning?","Question",{"text":75,"@type":76},"It addresses simultaneous adaptation to nonlinear domain shifts in streaming settings while mitigating catastrophic effects caused by severe, uncalibrated label noise in training data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does FlatManifold achieve robustness to label noise?",{"text":80,"@type":76},"It projects high-dimensional descriptors into a flattened structure using a Nyström kernel mapper and performs learning in an orthogonalized RKHS, where the flattened space acts as a smoothing barrier that diffuses corrupted gradients.",{"name":82,"@type":73,"acceptedAnswer":83},"How is catastrophic forgetting prevented during continual updates?",{"text":84,"@type":76},"A continual topology brake term leverages the covariance matrix of past experiences to constrain the evolution of the learned topology and preserve previously consolidated knowledge.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]