semi-supervised-learning

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

Algorithms for outlier, adversarial and drift detection

Unsupervised Data Augmentation (UDA)

FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai

😎 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.

A state-of-the-art semi-supervised method for image recognition

A Unified Semi-Supervised Learning Codebase (NeurIPS'22)

A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning.

A PyTorch-based library for semi-supervised learning (NeurIPS'21)

Build, train, optimize, and run computer vision models locally, from raw images to live inference. Open source, optimized for Intel® XPU (CUDA also supported).

Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks

Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.

Semi-Supervised Learning, Object Detection, ICCV2021

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"

A contrastive learning based semi-supervised segmentation network for medical image segmentation

[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

Implementations of various VAE-based semi-supervised and generative models in PyTorch