pytorch-adapt
Transfer-Learning-Library
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| pytorch-adapt | Transfer-Learning-Library | |
|---|---|---|
| 3 | 1 | |
| 323 | 3,144 | |
| - | 3.8% | |
| 0.0 | 6.9 | |
| about 1 year ago | about 1 month ago | |
| Python | Python | |
| MIT License | MIT License |
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pytorch-adapt
- PyTorch-adapt re-purposes existing ML models to work in new domains
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[P] A domain adaptation library that I wrote: PyTorch Adapt
For more details, you might be interested in the jupyter notebooks here: https://github.com/KevinMusgrave/pytorch-adapt/blob/main/examples/README.md
I wrote [a library](https://github.com/KevinMusgrave/pytorch-adapt) for domain adaptation, which is a type of machine learning that repurposes existing models to work in new domains. A toy example is adapting a model trained on MNIST for use on colored digits:
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.
What are some alternatives?
hierarchical-domain-adaptation - Code of NAACL 2022 "Efficient Hierarchical Domain Adaptation for Pretrained Language Models" paper.
TranAD - [VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.
pykale - Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem. ⭐ Star to support our work!
DeepLabCut - Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
CEPC - A domain adaptation model
transferlearning - Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
autogluon - AutoGluon: AutoML for Image, Text, Time Series, and Tabular Data [Moved to: https://github.com/autogluon/autogluon]
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)
DA-Faster-RCNN - Detectron2 implementation of DA-Faster R-CNN, Domain Adaptive Faster R-CNN for Object Detection in the Wild
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.