Transfer-Learning-Library
TranAD
| Transfer-Learning-Library | TranAD | |
|---|---|---|
| 1 | 1 | |
| 3,150 | 464 | |
| 2.2% | 4.3% | |
| 6.9 | 2.9 | |
| about 1 month ago | 6 months ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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Transfer-Learning-Library
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[R] pytorch library for audio/speech domain adaptation?
Are there any pytorch libraries to do benchmarking of domain adaptation methods for audio/speech tasks? Something like the Transfer Learning Library (https://github.com/thuml/Transfer-Learning-Library/) for images.
TranAD
What are some alternatives?
DeepLabCut - Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
anomaly-detection-resources - Anomaly detection related books, papers, videos, and toolboxes
transferlearning - Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
ADBench - Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.
CEPC - A domain adaptation model
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
AdaTime - [TKDD 2023] AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
StyleDomain - Official Implementation for "StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation" (ICCV 2023)
AugMax - [NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.
pytorch-adapt - Domain adaptation made easy. Fully featured, modular, and customizable.
DA-Faster-RCNN - Detectron2 implementation of DA-Faster R-CNN, Domain Adaptive Faster R-CNN for Object Detection in the Wild