contrastive-learning
There are 647 repositories under contrastive-learning topic.
google-research/simclr
SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
lightly-ai/lightly
A python library for self-supervised learning on images.
salesforce/ALBEF
Code for ALBEF: a new vision-language pre-training method
vturrisi/solo-learn
solo-learn: a library of self-supervised methods for visual representation learning powered by Pytorch Lightning
wvangansbeke/Unsupervised-Classification
SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
OpenGVLab/InternVideo
[ECCV2024] Video Foundation Models & Data for Multimodal Understanding
tangxyw/RecSysPapers
推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.
yzhuoning/Awesome-CLIP
Awesome list for research on CLIP (Contrastive Language-Image Pre-Training).
lucidrains/CoCa-pytorch
Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch
JosephKJ/OWOD
(CVPR 2021 Oral) Open World Object Detection
unum-cloud/uform
Pocket-Sized Multimodal AI for content understanding and generation across multilingual texts, images, and 🔜 video, up to 5x faster than OpenAI CLIP and LLaVA 🖼️ & 🖋️
AngeLouCN/Min_Max_Similarity
A contrastive learning based semi-supervised segmentation network for medical image segmentation
tensorflow/similarity
TensorFlow Similarity is a python package focused on making similarity learning quick and easy.
dailenson/SDT
This repository is the official implementation of Disentangling Writer and Character Styles for Handwriting Generation (CVPR 2023)
AdaptiveMotorControlLab/CEBRA
Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
PyGCL/PyGCL
PyGCL: A PyTorch Library for Graph Contrastive Learning
Spijkervet/SimCLR
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations by T. Chen et al.
lucidrains/x-clip
A concise but complete implementation of CLIP with various experimental improvements from recent papers
tfzhou/ContrastiveSeg
ICCV2021 (Oral) - Exploring Cross-Image Pixel Contrast for Semantic Segmentation
qdrant/quaterion
Blazing fast framework for fine-tuning similarity learning models
Westlake-AI/openmixup
CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark
MishaLaskin/curl
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
salesforce/PCL
PyTorch code for "Prototypical Contrastive Learning of Unsupervised Representations"
Shen-Lab/GraphCL
[NeurIPS 2020] "Graph Contrastive Learning with Augmentations" by Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen
ryanzhumich/Contrastive-Learning-NLP-Papers
Paper List for Contrastive Learning for Natural Language Processing
chaitjo/efficient-gnns
Code and resources on scalable and efficient Graph Neural Networks
nomic-ai/contrastors
Train Models Contrastively in Pytorch
RElbers/info-nce-pytorch
PyTorch implementation of the InfoNCE loss for self-supervised learning.
yxuansu/SimCTG
[NeurIPS'22 Spotlight] A Contrastive Framework for Neural Text Generation
mims-harvard/TFC-pretraining
Self-supervised contrastive learning for time series via time-frequency consistency
Haochen-Wang409/U2PL
[CVPR'22] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
wvangansbeke/Unsupervised-Semantic-Segmentation
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]
HKUDS/SSLRec
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
JohnGiorgi/DeCLUTR
The corresponding code from our paper "DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations". Do not hesitate to open an issue if you run into any trouble!
SXKDZ/awesome-self-supervised-learning-for-graphs
A curated list for awesome self-supervised learning for graphs.
emadeldeen24/TS-TCC
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"