An open source implementation of CLIP.
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Updated
Aug 19, 2024 - Python
An open source implementation of CLIP.
Siamese and triplet networks with online pair/triplet mining in PyTorch
Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.
Keras implementation of Representation Learning with Contrastive Predictive Coding
A PyTorch implementation of SimCLR based on ICML 2020 paper "A Simple Framework for Contrastive Learning of Visual Representations"
official implementation of the spatial-temporal attention neural network (STANet) for remote sensing image change detection
Contrastive Predictive Coding for Automatic Speaker Verification
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
pytorch implementation of scene change detection
A general representation model across vision, audio, language modalities. Paper: ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
PyTorch implementation of the InfoNCE loss for self-supervised learning.
[ECCV 2022 Oral] Official Pytorch implementation of CCPL and SCTNet
[ICRA 2022] The official repository for "LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition", In 2022 International Conference on Robotics and Automation (ICRA), pp. 2215-2221.
A simple to use pytorch wrapper for contrastive self-supervised learning on any neural network
PyTorch implementation for Partially View-aligned Representation Learning with Noise-robust Contrastive Loss (CVPR 2021)
Official implementation for "Image Quality Assessment using Contrastive Learning"
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)
Similarity Learning applied to Speaker Verification and Semantic Textual Similarity
The implement for paper : "Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval"
TensorFlow Implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
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