Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
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Updated
May 10, 2024 - Python
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
[ICLR-2020] Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification.
[CVPR22] Official Implementation of DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
PyTorch open-source toolbox for unsupervised or domain adaptive object re-ID.
pytorch implementation for Contrastive Adaptation Network
[ECCV22] Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
Implementation of ECCV 2020 paper "Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector"
[TKDD 2023] AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation (CVPR 2021)
Code for ICML2020 "Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation"
An official implementation of ICML 2022 paper "Learning Domain Adaptive Object Detection with Probabilistic Teacher"."
Deep learning research implemented on notebooks using PyTorch.
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night
Official Detectron2 implementation of DA-RetinaNet of our Image and Vision Computing 2021 work 'An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites'
NeurIPS 2023: Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling
Detectron2 implementation of DA-Faster R-CNN, Domain Adaptive Faster R-CNN for Object Detection in the Wild
This is a PyTorch implementation of the Unsupervised Domain Adaptation method proposed in the paper Deep CORAL: Correlation Alignment for Deep Domain Adaptation. Baochen Sun and Kate Saenko (ECCV 2016).
(RA-L 2022) See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation.
Code and reuslts accompanying the NeurIPS 2022 paper with the title SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
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