A collection of AWESOME things about domian adaptation
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Updated
Jun 11, 2022
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A collection of AWESOME things about domian adaptation
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
Must-read Papers on Textual Adversarial Attack and Defense
A Toolbox for Adversarial Robustness Research
[CVPR 2018] Look at Boundary: A Boundary-Aware Face Alignment Algorithm
Next RecSys Library
Training neural models with structured signals.
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
Domain-Adversarial Neural Network in Tensorflow
Adversarial Learning for Semi-supervised Semantic Segmentation, BMVC 2018
[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation
Implementation of Papers on Adversarial Examples
( TPAMI2021 / CVPR2019 Oral ) Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
A curated list of dedicated resources and applications
An Unsupervised Learning Framework for Moving Object Detection From Videos
Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow
[ECCV 2018] ReenactGAN: Learning to Reenact Faces via Boundary Transfer
SegAN: Semantic Segmentation with Adversarial Learning
Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural training.
A Paper List for Open-Domain Dialogue Generation, and related datasets.
Code for the ACL paper "No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling"
[CVPR 2020] Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
PyTorch library for adversarial attack and training
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Implementation of ECCV 2020 paper "Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector"
Pytorch implementation of Virtual Adversarial Training
A Pytorch implementation for the ZeroSpeech 2019 challenge.
[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.
[VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.
Code for "Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation" (CVPR 2018)
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