Projects sharing features with Cleanlab

WACV'21: Do We Really Need Gold Samples for Sample Weighting Under Label Noise?

Q. Yao, H. Yang, B. Han, G. Niu, J. Kwok. Searching to Exploit Memorization Effect in Learning from Noisy Labels. ICML 2020

Implementation of experiments in paper "Learning from Rules Generalizing Labeled Exemplars" to appear in ICLR2020 (https://openreview.net/forum?id=SkeuexBtDr)

Auxiliary Image Regularization for Deep CNNs with Noisy labels at ICLR 2016

Official PyTorch implementation of "From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model" (ICML 2022) by HeeSun Bae, Seungjae Shin, Byeonghu Na, JoonHo Jang, Kyungwoo Song, and Il-Chul Moon.

This repository contains the pytroch code to reproduce the results the paper "Wasserstein Adversarial Regularization for Learning with Label Noise"

NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

NeurIPS'18: Masking: A New Perspective of Noisy Supervision

ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust

The code for our WACV 2023 submission "Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels".

SREA: Self-Re-Labeling with Embedding Analysis

🧹 Formerly for binary classification with noisy labels. Replaced by cleanlab.

AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise

AAAI 2021: Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels

🛠️ Corrected Test Sets for ImageNet, MNIST, CIFAR, Caltech-256, QuickDraw, IMDB, Amazon Reviews, 20News, and AudioSet

PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"

An official implementation of "Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation" (CVPR 2021) in PyTorch.

Joint Optimization Framework for Learning with Noisy Labels

Code for "Adaptive Sample Selection for Robust Learning under Label Noise" accepted at IEEE/CVF WACV 2023.

InsightFace is a comprehensive deep learning framework designed for face recognition, biometric identity verification, and feature extraction. It provides a specialized engine for one-to-one verification and one-to-many identification tasks, utilizing convolutional neural networks to transform raw image pixels into high-dimensional vector embeddings. The project includes a complete toolkit for detecting, aligning, and processing facial data to ensure consistent identity discrimination. Beyond core recognition, the platform distinguishes itself through an extensive model management and optimiz

This project is an AI research implementation library and machine learning research repository. It provides a collection of reference code, illustrative implementations, and open-source research datasets used to verify hypotheses and build upon existing models in artificial intelligence. The repository focuses on scientific research reproduction by translating theoretical findings from published papers into executable code. It includes specialized scientific simulation environments designed to test the behavior of autonomous agents and models within controlled settings. The project covers AI

Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL)

This repo is the PyTorch codes for "Improving Unsupervised Image Clustering With Robust Learning (RUC)"

Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for deep learning models. It provides a collection of transformations that modify pixel values and spatial geometry to increase the diversity of training samples and improve model generalization. The library supports both 2D image augmentation and 3D volumetric data augmentation. It handles a variety of labels alongside images, ensuring that bounding boxes, keypoints, and segmentation masks remain accurately aligned when spatial transformations are applied. The tool incorporates