Transfer-Learning-Library VS pytorch-adapt

Compare Transfer-Learning-Library vs pytorch-adapt and see what are their differences.

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Transfer-Learning-Library pytorch-adapt
1 3
3,150 323
2.2% -
6.9 0.0
about 1 month ago over 1 year ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Transfer-Learning-Library

Posts with mentions or reviews of Transfer-Learning-Library. We have used some of these posts to build our list of alternatives and similar projects.

pytorch-adapt

Posts with mentions or reviews of pytorch-adapt. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing Transfer-Learning-Library and pytorch-adapt you can also consider the following projects:

TranAD - [VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.

hierarchical-domain-adaptation - Code of NAACL 2022 "Efficient Hierarchical Domain Adaptation for Pretrained Language Models" paper.

DeepLabCut - Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans

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!

transferlearning - Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

CEPC - A domain adaptation model

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.