Image Classification with Label Noise
8 papers with code • 16 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Image Classification with Label Noise
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CIFAR-10
CIFAR-100
WikiChurches
Corn Seeds Dataset
Most implemented papers
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
UCSC-REAL/HOC
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Nonetheless, finding anchor points remains a non-trivial task, and the estimation accuracy is also often throttled by the number of available anchor points.
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
This high-quality sample sieve allows us to treat clean examples and the corrupted ones separately in training a DNN solution, and such a separation is shown to be advantageous in the instance-dependent noise setting.
A Second-Order Approach to Learning with Instance-Dependent Label Noise
We first provide evidences that the heterogeneous instance-dependent label noise is effectively down-weighting the examples with higher noise rates in a non-uniform way and thus causes imbalances, rendering the strategy of directly applying methods for class-dependent label noise questionable.
NVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification
fbladl/nvum
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In this paper, we propose a new training module called Non-Volatile Unbiased Memory (NVUM), which non-volatility stores running average of model logits for a new regularization loss on noisy multi-label problem.
Adaptive 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.
PropMix: 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.
A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?
glhr/ood-labelnoise
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CVPR 2024
The ability to detect unfamiliar or unexpected images is essential for safe deployment of computer vision systems.
Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty
Dataset pruning aims to alleviate this demand by discarding redundant examples.
