Transfer-Learning-Library
DA-Faster-RCNN
| Transfer-Learning-Library | DA-Faster-RCNN | |
|---|---|---|
| 1 | 1 | |
| 3,150 | 47 | |
| 2.2% | - | |
| 6.9 | 5.4 | |
| about 1 month ago | 8 months ago | |
| Python | Python | |
| MIT License | MIT 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.
DA-Faster-RCNN
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[R] DA-Faster RCNN
I have reimplemented DA-Faster RCNN using Detectron2 one of the most important architecture for domain adaptation for object detection. This implementations is easy to use and can be used also with google colab :) here there is the link: https://github.com/GiovanniPasq/DA-Faster-RCNN
What are some alternatives?
TranAD - [VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.
DA-RetinaNet - 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'
DeepLabCut - Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
globox - A package to read and convert object detection datasets (COCO, YOLO, PascalVOC, LabelMe, CVAT, OpenImage, ...) and evaluate them with COCO and PascalVOC metrics.
transferlearning - Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
mmdetection - OpenMMLab Detection Toolbox and Benchmark
CEPC - A domain adaptation model
AdaTime - [TKDD 2023] AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data
StyleDomain - Official Implementation for "StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation" (ICCV 2023)
pytorch-adapt - Domain adaptation made easy. Fully featured, modular, and customizable.
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.