#Contrastive-learning
Showing 39 of 39 repositories tagged #contrastive-learning, ranked by stars
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
A python library for self-supervised learning on images.
All-in-one training for vision models (YOLO, ViTs, RT-DETR, DINOv3): pretraining, fine-tuning, distillation.
This repository is the official implementation of Disentangling Writer and Character Styles for Handwriting Generation (CVPR 2023)
Pocket-Sized Multimodal AI for content understanding and generation across multilingual texts, images, and 🔜 video, up to 5x faster than OpenAI CLIP and LLaVA 🖼️ & 🖋️
Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch
Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
PyGCL: A PyTorch Library for Graph Contrastive Learning
Train Models Contrastively in Pytorch
A concise but complete implementation of CLIP with various experimental improvements from recent papers
This repo contains the code for "VLM2Vec" [ICLR 2025], "VLM2Vec-V2 [TMLR 2026]", and "MMEB-V3"
Blazing fast framework for fine-tuning similarity learning models
CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
Paper List for Contrastive Learning for Natural Language Processing
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
Self-supervised contrastive learning for time series via time-frequency consistency
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
[CVPR'22 & IJCV'24] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels & Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation
LibAUC: A Deep Learning Library for X-Risk Optimization
A list of contrastive Learning papers
Reliable, minimal and scalable library for pretraining foundation and world models
Implementation of Pixel-level Contrastive Learning, proposed in the paper "Propagate Yourself", in Pytorch
[WACV 2024 Oral] - ARNIQA: Learning Distortion Manifold for Image Quality Assessment
Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications
Official Implementation of PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
mPLUG-HalOwl: Multimodal Hallucination Evaluation and Mitigating
[TPAMI & ECCV 2022] Contrast-Phys & Contrast-Phys+ for facial video-based remote physiological signal measurement
Code for the paper "Contrastive Learning Inverts the Data Generating Process".
FlatNCE: A Novel Contrastive Representation Learning Objective
Independent implementation of Supervised Contrastive Loss. Straight to the point and beyond
Multimodal Semi-Supervised Learning for Text Recognition (SemiMTR)
[CVPR2024] Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation
All-in-One: Text Embedding, Retrieval, Reranking and RAG in Transformers
Contrastive Reinforcement Learning
CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations, ICCV 2021
[ACL 2023] Code for ContraCLM: Contrastive Learning For Causal Language Model
Repository for "Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models" (ApJ) and "Generating astronomical spectra from photometry with conditional diffusion models" (ML4PS@NeurIPS 2022)