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Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation

https://doi.org/10.1109/cvpr52688.2022.01613

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What this work says

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Table of Contents

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Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning

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Federated Learning with Instance-Dependent Noisy Label

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Learning From Single-Expert Annotated Labels for Automatic Sleep Staging

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Learning from Noisy Data Using Pretrained Vision-Language Representations

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Deep Learning Under Instance-Dependent Label Noise: A Tensor Factorization Perspective

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A Joint Training and Confidence Calibration Procedure That is Robust to Label Noise

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A Novel Industrial Defect Recognition Method Under Label Noise Based on Parallel Hybrid Penalty Network

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Prototype-Oriented Clean Subset Extraction for Noisy Long-Tailed Classification

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Weakly Supervised Solar Panel Mapping via Uncertainty Adjusted Label Transition in Aerial Images

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Streaming View Classification With Noisy Label

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Confidence Calibration of a Medical Imaging Classification System That is Robust to Label Noise

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QMix: Quality-Aware Learning With Mixed Noise for Robust Retinal Disease Diagnosis

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Learning With Imbalanced Noisy Data by Preventing Bias in Sample Selection

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Toward Robust Semi-Supervised Distribution Alignment Against Label Distribution Shift With Noisy Annotations

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Cognition-Driven Structural Prior for Instance-Dependent Label Transition Matrix Estimation

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Adaptive Learning for Dynamic Features and Noisy Labels

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Trusted Multi-View Learning Under Noisy Supervision

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Learning to Learn from Corrupted Data for Few-Shot Learning

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Stochastic Feature Averaging for Learning with Long-Tailed Noisy Labels

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Weighted Contrastive Prototype Network for Few-Shot Hyperspectral Image Classification with Noisy Labels

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Biquality learning : from weakly supervised learning to distribution shifts

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