| thuml/Transfer-Learning-Library |
2,883 |
|
0 |
0 |
over 2 years ago |
2 |
July 24, 2020 |
28 |
mit |
Python |
| Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization |
| pumpikano/tf-dann |
580 |
|
0 |
0 |
over 4 years ago |
0 |
|
10 |
mit |
Jupyter Notebook |
| Domain-Adversarial Neural Network in Tensorflow |
| cs-chan/Exclusively-Dark-Image-Dataset |
461 |
|
0 |
0 |
over 2 years ago |
0 |
|
8 |
bsd-3-clause |
MATLAB |
| Exclusively Dark (ExDARK) dataset which to the best of our knowledge, is the largest collection of low-light images taken in very low-light environments to twilight (i.e 10 different conditions) to-date with image class and object level annotations. |
| jvanvugt/pytorch-domain-adaptation |
357 |
|
0 |
0 |
over 4 years ago |
0 |
|
6 |
mit |
Python |
| A collection of implementations of adversarial domain adaptation algorithms |
| wenbowen123/iros20-6d-pose-tracking |
299 |
|
0 |
0 |
over 2 years ago |
0 |
|
4 |
other |
Python |
| [IROS 2020] se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains |
| fungtion/DANN_py3 |
298 |
|
0 |
0 |
almost 4 years ago |
0 |
|
2 |
mit |
Python |
| python 3 pytorch implementation of DANN |
| tim-learn/SHOT |
279 |
|
0 |
0 |
over 3 years ago |
0 |
|
0 |
mit |
Python |
| code released for our ICML 2020 paper "Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation" |
| xuanyuzhou98/SqueezeSegV2 |
205 |
|
0 |
0 |
over 3 years ago |
0 |
|
20 |
bsd-2-clause |
Python |
| Implementation of SqueezeSegV2, Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud |
| thuml/Universal-Domain-Adaptation |
190 |
|
0 |
0 |
over 4 years ago |
0 |
|
7 |
|
Python |
| Code release for Universal Domain Adaptation(CVPR 2019) |
| bupt-ai-cz/Meta-SelfLearning |
175 |
|
0 |
0 |
over 3 years ago |
0 |
|
0 |
|
Python |
| Meta Self-learning for Multi-Source Domain Adaptation： A Benchmark |
