TranAD VS Transfer-Learning-Library

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

TranAD

[VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training. (by imperial-qore)
anomaly-detection transformer-models multi-head-attention unsupervised-learning adversarial-learning
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Transfer-Learning-Library

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization (by thuml)
domain-adaptation transfer-learning finetune unsupervised-domain-adaptation image-translation adversarial-learning Deep Learning self-training semi-supervised-learning Dann out-of-distribution-generalization
Source Code
transfer.thuml.ai
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TranAD Transfer-Learning-Library
1 1
462 3,144
7.6% 3.8%
2.9 6.9
6 months ago about 1 month ago
Python Python
BSD 3-clause "New" or "Revised" License MIT License
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TranAD

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

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.

What are some alternatives?

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

anomaly-detection-resources - Anomaly detection related books, papers, videos, and toolboxes

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

ADBench - Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.

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

pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

CEPC - A domain adaptation model

anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

AdaTime - [TKDD 2023] AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data

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.

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.

DA-Faster-RCNN - Detectron2 implementation of DA-Faster R-CNN, Domain Adaptive Faster R-CNN for Object Detection in the Wild

TranAD vs anomaly-detection-resources Transfer-Learning-Library vs DeepLabCut TranAD vs ADBench Transfer-Learning-Library vs transferlearning TranAD vs pyod Transfer-Learning-Library vs CEPC TranAD vs anomalib Transfer-Learning-Library vs AdaTime TranAD vs AugMax Transfer-Learning-Library vs StyleDomain Transfer-Learning-Library vs pytorch-adapt Transfer-Learning-Library vs DA-Faster-RCNN