Who leads the noisy-mnist benchmark?

PCGAN-CHAR currently leads noisy-mnist with a score of 98.43 on Accuracy.

What is the state-of-the-art score on noisy-mnist?

The state-of-the-art result on noisy-mnist is 98.43 (Accuracy), achieved by PCGAN-CHAR as of 2019.

How many models are tracked on noisy-mnist?

Codesota tracks 1 model on noisy-mnist.

When was the noisy-mnist leaderboard last updated?

The noisy-mnist leaderboard on Codesota includes results through 2019.

Codesota · Benchmark · noisy-mnistHome/Leaderboards/noisy-mnist

Unknown

noisy-mnist.

Name: noisy-mnist Benchmark Results
Creator: Unknown
Published: 2019-01-01
License: https://creativecommons.org/licenses/by/4.0/

noisy-mnist is a state-of-the-art machine learning benchmark indexed on Codesota. This page tracks published model results, top scores per metric, and the SOTA timeline for noisy-mnist.

Paper ↗Leaderboard ↓

§ 01 · Leaderboard

Results by metric.

Only 1 model on this benchmark

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Accuracy

Accuracy is the reported evaluation metric for noisy-mnist. Codesota tracks published model scores on this metric so readers can compare state-of-the-art results across sources and model families.

Higher is better

Trust tiers for Accuracyverifiedpapervendorcommunityunverified

Rank	Model	Trust	Score	Year	Links	Fix
01	PCGAN-CHAR From paper: PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters	verified	98.43	2019	Paper ↗	Looks wrong?

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