Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
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Updated
May 10, 2024 - Python
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
Algorithms for outlier, adversarial and drift detection
Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
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-based library for semi-supervised learning (NeurIPS'21)
A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning.
Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
A contrastive learning based semi-supervised segmentation network for medical image segmentation
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
Semi-Supervised Learning, Object Detection, ICCV2021
Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"
[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning
Implementations of various VAE-based semi-supervised and generative models in PyTorch
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
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