Image Classification with Label Noise

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

8 shown of 8 papers with code (10 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
  10 Feb 2021 2 repositories listed Syntology ran 0 of 9 samples · 9 unverified
  Nonetheless, finding anchor points remains a non-trivial task, and the estimation accuracy is also often throttled by the number of available anchor points.
• Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty
  10 Feb 2025 1 repository listed
  Dataset pruning aims to alleviate this demand by discarding redundant examples.
• A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?
  2 Apr 2024 1 repository listed Syntology ran 8 of 11 samples · 3 unverified · 11 pointer-only (licence)
  The ability to detect unfamiliar or unexpected images is essential for safe deployment of computer vision systems.
• PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels
  22 Oct 2021 1 repository listed
  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.
• Adaptive Sample Selection for Robust Learning under Label Noise
  29 Jun 2021 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the presence of noisily-labelled data.
• NVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification
  6 Mar 2021 1 repository listed
  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.
• A Second-Order Approach to Learning with Instance-Dependent Label Noise
  22 Dec 2020 1 repository listed
  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…
• Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
  5 Oct 2020 1 repository listed Syntology ran 2 of 4 samples · 2 unverified · 4 pointer-only (licence)
  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.

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.