#Semi-supervised-learning
Showing 39 of 39 repositories tagged #semi-supervised-learning, ranked by stars
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
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 computer vision models in a fraction of the time and with less data.
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
Semi-Supervised Learning, Object Detection, ICCV2021
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark
Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"
A curated collection of adversarial attack and defense on graph data.
Code for the AAAI 2018 publication "SEE: Towards Semi-Supervised End-to-End Scene Text Recognition"
Adversarial Learning for Semi-supervised Semantic Segmentation, BMVC 2018
Code for the paper STN-OCR: A single Neural Network for Text Detection and Text Recognition
[CVPR'22 & IJCV'24] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels & Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation
:page_facing_up: Semi-Supervised Semantic Segmentation with Cross-Consistency Training (CVPR 2020).
A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.
Learning to Cluster. A deep clustering strategy.
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)
I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers
30 Semi-Supervised Learning Algorithms
Pytorch implementation of Noisy Student Training for Automatic Speech Recognition and Automatic Pronunciation Error Detection problem
Pytorch implementations of generative models: VQVAE2, AIR, DRAW, InfoGAN, DCGAN, SSVAE
[ECCV 2020] Full-Body Awareness from Partial Observations
Code accompanying the paper "Semi-supervised adversarial audio source separation applied to singing voice extraction"
🧹 Formerly for binary classification with noisy labels. Replaced by cleanlab.
Multimodal Semi-Supervised Learning for Text Recognition (SemiMTR)
PyTorch implementation of Adversarially Learned Inference (BiGAN).
[ICLR 2024] SemiReward: A General Reward Model for Semi-supervised Learning
"Towards Semi-supervised Learning with Non-random Missing Labels" by Yue Duan (ICCV 2023)
[NeurIPS 2023 Main Track] This is the repository for the paper titled "Don’t Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner"
[ECCV 2022] Official Implementation for Unsupervised Selective Labeling for More Effective Semi-Supervised Learning
Code for the paper: "SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification"
Code for the paper "MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels"
TFG - Semisupervised learning and instance selection methods