Popular GitHub Repositories for Noisy Labels

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

Laminar - open-source all-in-one platform for engineering AI products. Crate data flywheel for you AI app. Traces, Evals, Datasets, Labels. YC S24.

Simple project to detect objects and display 3D labels above them in AR. This serves as a basic Template for an ARKit project to use CoreML.

SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

A Light CNN for Deep Face Representation with Noisy Labels, TIFS 2018

Curated list of open source tooling for data-centric AI on unstructured data.

Official repository for the paper "High-Resolution Daytime Translation Without Domain Labels" (CVPR2020, Oral)

A machine learning tool for automated prediction engineering. It allows you to easily structure prediction problems and generate labels for supervised learning.

Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st place on KITTI) [MVA 2019]

[CVPR'22 & IJCV'24] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels & Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation

The toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling.

A collection of 3 lambda functions that are invoked by Amazon S3 or Amazon API Gateway to analyze uploaded images with Amazon Rekognition and save picture labels to ElasticSearch (written in Kotlin)

Code For The Issue Label Bot, an App that automatically labels issues using machine learning, available on the GitHub Marketplace. This is also code for the blog article: "How to automate tasks on GitHub with machine learning for fun and profit"

DiffSeg is an unsupervised zero-shot segmentation method using attention information from a stable-diffusion model. This repo implements the main DiffSeg algorithm and additionally includes an experimental feature to add semantic labels to the masks based on a generated caption.

[ECCV'22] Official repository of paper titled "Class-agnostic Object Detection with Multi-modal Transformer".

PyTorch implementation of Graph Matching Networks, e.g., Graph Matching with Bi-level Noisy Correspondence (COMMON, ICCV 2023), Graph Matching Networks for Learning the Similarity of Graph Structured Objects (GMN, ICML 2019).

Synthetic Image generation with Flip. Generate thousands of new 2D images from a small batch of objects and backgrounds.

Implementations of algorithms from the Q-learning family. Implementations inlcude: DQN, DDQN, Dueling DQN, PER+DQN, Noisy DQN, C51