Home › Datasets › task › Learning with noisy labels
Learning with noisy labels datasets
archive 2025-07-28
16 datasets carry the task tag "Learning with noisy labels" (the task itself: Learning with noisy labels), ordered by the archive's paper count. Page 1 of 1: 16 shown of 16. Facet routes are this site's own (the archive records the tag string, not a page).
The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.
Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets
Learning with noisy labels datasets 1–16 of 16
description withheld: archive row vandalised before snapshot
16,145 papers · 91 benchmarks
The CIFAR-100 dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images.
9,045 papers · 51 benchmarks
The Food-101 dataset consists of 101 food categories with 750 training and 250 test images per category, making a total of 101k images.
805 papers · 14 benchmarks
VoxCeleb1 is an audio dataset containing over 100,000 utterances for 1,251 celebrities, extracted from videos uploaded to YouTube.
680 papers · 10 benchmarks
Clothing1M contains 1M clothing images in 14 classes.
288 papers · 4 benchmarks
The WebVision dataset is designed to facilitate the research on learning visual representation from noisy web data.
179 papers · 4 benchmarks
This work presents two new benchmark datasets (CIFAR-10N, CIFAR-100N), equipping the training dataset of CIFAR-10 and CIFAR-100 with human-annotated real-world noisy labels that we collect from Amazon Mechanical Turk.
97 papers · 6 benchmarks
This work presents two new benchmark datasets (CIFAR-10N, CIFAR-100N), equipping the training dataset of CIFAR-10 and CIFAR-100 with human-annotated real-world noisy labels that we collect from Amazon Mechanical Turk.
66 papers · 1 benchmark
Chaoyang dataset contains 1111 normal, 842 serrated, 1404 adenocarcinoma, 664 adenoma, and 705 normal, 321 serrated, 840 adenocarcinoma, 273 adenoma samples for training and testing, respectively.
19 papers · 2 benchmarks
10 classes with 50, 000 training and 5, 000 testing images.
15 papers · 1 benchmark
Part of the Controlled Noisy Web Labels Dataset.
6 papers · 2 benchmarks
Part of the Controlled Noisy Web Labels Dataset.
6 papers · 2 benchmarks
Part of the Controlled Noisy Web Labels Dataset.
6 papers · 2 benchmarks
COCO-N Medium introduces a stochastic benchmark that simulates common real-world scenarios with noticeable label inaccuracies in the COCO dataset.
1 paper · 1 benchmark
The COCO-WAN benchmark is designed to assess the impact of weakly annotations (combined with auto-annotation tools) noise on instance segmentation models.
1 paper · 1 benchmark
Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.