Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
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Updated
Feb 27, 2023 - Python
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
Unsupervised Data Augmentation (UDA)
Algorithms for outlier, adversarial and drift detection
FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
A state-of-the-art semi-supervised method for image recognition
Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
A PyTorch-based library for semi-supervised learning (NeurIPS'21)
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
Semi-Supervised Learning, Object Detection, ICCV2021
A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning.
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
Implementations of various VAE-based semi-supervised and generative models in PyTorch
[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning
A Unified Semi-Supervised Learning Codebase (NeurIPS'22)
Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"
Code for the AAAI 2018 publication "SEE: Towards Semi-Supervised End-to-End Scene Text Recognition"
Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"
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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