Papers Image Classification with Label Noise
“Image Classification with Label Noise” 태그가 달린 논문 10편 · 필터 해제
Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty
Recent advances in deep learning rely heavily on massive datasets, leading to substantial storage and training costs. Dataset pruning aims to alleviate this demand by discarding redundant examples. However, many existing…
image-classificationImage ClassificationImage Classification with Label NoiseA noisy elephant in the room: Is your out-of-distribution detector robust to label noise?
The ability to detect unfamiliar or unexpected images is essential for safe deployment of computer vision systems. In the context of classification, the task of detecting images outside of a model's training domain is kn…
Image ClassificationImage Classification with Label NoiseOut of Distribution (OOD) DetectionDeep Active Learning in the Presence of Label Noise: A Survey
Deep active learning has emerged as a powerful tool for training deep learning models within a predefined labeling budget. These models have achieved performances comparable to those trained in an offline setting. Howeve…
Active LearningDiversityimage-classificationImage Classification+1PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels
The most competitive noisy label learning methods rely on an unsupervised classification of clean and noisy samples, where samples classified as noisy are re-labelled and "MixMatched" with the clean samples. These method…
Image ClassificationImage Classification with Label NoiseLearning with noisy labelsAdaptive Sample Selection for Robust Learning under Label Noise
Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the presence of noisily-labelled data. For the problem of robust learning under such noisy data, several algorithms have bee…
Image ClassificationImage Classification with Label NoiseMemorizationNVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification
Real-world large-scale medical image analysis (MIA) datasets have three challenges: 1) they contain noisy-labelled samples that affect training convergence and generalisation, 2) they usually have an imbalanced distribut…
image-classificationImage Classification with Label NoiseLearning with noisy labelsMedical Image Analysis+1Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation
Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognition even under a high noisy rate. In med…
BenchmarkingGeneral Classificationimage-classificationImage Classification+2Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
The label noise transition matrix, characterizing the probabilities of a training instance being wrongly annotated, is crucial to designing popular solutions to learning with noisy labels. Existing works heavily rely on …
Image ClassificationImage Classification with Human NoiseImage Classification with Label NoiseLearning with noisy labelsA Second-Order Approach to Learning with Instance-Dependent Label Noise
The presence of label noise often misleads the training of deep neural networks. Departing from the recent literature which largely assumes the label noise rate is only determined by the true label class, the errors in h…
Image ClassificationImage Classification with Label NoiseLearning with Instance-Dependent Label Noise: A Sample Sieve Approach
Human-annotated labels are often prone to noise, and the presence of such noise will degrade the performance of the resulting deep neural network (DNN) models. Much of the literature (with several recent exceptions) of l…
Image ClassificationImage Classification with Label NoiseLearning with noisy labels