| KevinMusgrave/powerful-benchmarker |
426 |
|
0 |
0 |
over 2 years ago |
34 |
September 19, 2020 |
3 |
|
Jupyter Notebook |
| A library for ML benchmarking. It's powerful. |
| 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 |
| cleardusk/MFR |
134 |
|
0 |
0 |
over 4 years ago |
0 |
|
4 |
|
|
| Learning Meta Face Recognition in Unseen Domains, CVPR, Oral, 2020 |
| ruidan/DAS |
34 |
|
0 |
0 |
over 7 years ago |
0 |
|
1 |
apache-2.0 |
Python |
| Code and datasets for EMNLP2018 paper ‘‘Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification’’. |
| agrija9/Deep-Unsupervised-Domain-Adaptation |
20 |
|
0 |
0 |
over 4 years ago |
0 |
|
1 |
|
Python |
| Pytorch implementation of four neural network based domain adaptation techniques: DeepCORAL, DDC, CDAN and CDAN+E. Evaluated on benchmark dataset Office31. |
| XiaoYee/ACAN |
12 |
|
0 |
0 |
almost 7 years ago |
0 |
|
0 |
mit |
Python |
| Code for NAACL 2019 paper: Adversarial Category Alignment Network for Cross-domain Sentiment Classification |
| UKPLab/useb |
12 |
|
0 |
0 |
over 4 years ago |
0 |
|
1 |
apache-2.0 |
Python |
| Heterogenous, Task- and Domain-Specific Benchmark for Unsupervised Sentence Embeddings used in the TSDAE paper: https://arxiv.org/abs/2104.06979. |
