A curated (most recent) list of resources for Learning with Noisy Labels

Labelling platform for text using weak supervision.

[NAACL 2021] This is the code for our paper `Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach'.

Weakly supervised medical named entity classification

PyTorch implementation for Learning with Twin Noisy Labels for Visible-Infrared Person Re-Identification (CVPR 2022).

A pytorch implementation for "Neighborhood Collective Estimation for Noisy Label Identification and Correction", which is accepted by ECCV2022.

Uncertainty-aware Fine-tuning of Segmentation Foundation Models (NeurIPS 2024).

Tensorflow 版本的图片鉴黄。not suitable/safe for work (NSFW) images detection using Tensorflow

Official code of "No Regret Sample Selection with Noisy Labels"

Learning with Noisy Labels for Sentence-level Sentiment Classification

Official PyTorch Implementation for the "Active Learning with a Noisy Annotator" paper.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise, NeurIPS 2024

nsfw-image-detection is a vision-language encoder model fine-tuned from siglip2-base-patch16-256 for multi-class image classification. Built on the SiglipForImageClassification architecture, the model is trained to identify and categorize content types in images, especially for explicit, suggestive, or safe media filtering.