arXiv Machine Learning July 7, 2026 By Rai Hisada, Kanji Tanaka

FlatManifold: Robust Continual Learning under Severe Label Noise and Domain Shifts via Intrinsic Manifold Flattening

Read the original on arXiv Machine Learning →

arXiv:2607. 05201v1 Announce Type: new Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

robotics

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Lifelong Representations: A Survey on Continual Self-Supervised Learning for Vision Models

arXiv:2607. 09785v1 Announce Type: cross Abstract: Traditionally, continual learning has assumed access to labeled data, yet many real-world applications -- such as lifelong robotics -- require models to adapt continuously from unlabeled streams.

By Sergi Masip, Alicja Dobrzeniecka, Jonathan Swinnen, Joachim Collin, Bart{\l}omiej Twardowski, Szymon {\L}ukasik, Tinne Tuytelaars

robotics efficiency multimodal benchmarks

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RODR: Riemannian Orthogonally Decoupled Regularization for Disentangled Manifold Representation

Point cloud denoising is essentially a geometric recovery task that aims to reconstruct the intrinsic structure of a smooth 2D Riemannian manifold embedded in R^3 from noisy, discrete ambient-space samples. Despite the remarkable progress of modern manifold-aware encoders and generative transport models in geometric representation learning, a fundamental objective-geometry mismatch remains underexplored.

benchmarks

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An Adaptive Data cleaning Framework for Noisy Label Detection

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computer-vision

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Fast Rates for Semi-Supervised Learning via Data-Augmentation Graph Regularization

arXiv:2607. 07513v1 Announce Type: new Abstract: Self-supervised learning matches supervised accuracy from a fraction of the labels, but the labeled-sample efficiency behind this has lacked a theoretical explanation.

By Adam M. Oberman

safety

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The Role of Pseudo-labels in Self-training Linear Classifiers on High-dimensional Gaussian Mixture Data

arXiv:2205. 07739v4 Announce Type: replace-cross Abstract: Self-training (ST) is a simple yet effective semi-supervised learning method.

By Takashi Takahashi

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Orthogonal Gradient Constraints Shape Noisy-Label Memorization Dynamics

arXiv:2607. 16231v1 Announce Type: new Abstract: Modern neural networks can fit corrupted training labels, making noisy-label learning a useful setting for studying memorization-driven overfitting.

By Richard Mai

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