It achieves superior results in unsupervised domain adaptation tasks, addressing the challenge of limited labeled data in real-world scenarios. The code and models are available at https://github.com/Jayee-chen/Adversarial-Domain-Adaptation.git....
In adversarial domain adaptation, this problem is usually solved by training an auxiliary model called the domain discriminator. The goal of this model is to classify examples as coming from the source or target distribution. The original classifier will then try to maximize the loss of the domain...
Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018) New version: https://github.com/thuml/Transfer-Learning-Library Dataset Digits Processed SVHN_dataset is here. We change the original mat into images. Other transformed images are in data/svhn2mnist and data/usps2mnist. Dat...
Gradually Vanishing Bridge for Adversarial Domain Adaptation 论文链接:https://openaccess.thecvf.com/content_CVPR_2020/papers/Cui_Gradually_Vanishing_Bridge_for_Adversarial_Domain_Adaptation_CVPR_2020_paper.pdf Code:https://github.com/cuishuhao/GVB Abstract 问题:在现有的解决方案中,领域差异被认为是直接...
本文主要介绍域自适应(Domain Adaptation)中的对抗域自适应方法(Domain Adversarial Learning)。 域自适应的算法不仅包括对抗域自适应方法,还包括: 统计距离(Statistics Matching) 假设对抗自适应(Hypothesis Adversarial) 数据域翻译(Domain Translation) 自训练(Self-Training) ...
Code release for "Multi-Adversarial Domain Adaptation" (AAAI 2018) Prerequisite Protobuf Version 2.6.1 CUDA 7.5/8.0 Modification on Caffe Add "OuterProduct" layer to calculate weighted feature to input to each domain adversarial network; Datasets ...
$ git clone https://github.com/valeoai/ADVENT $ cd ADVENT Install OpenCV if you don't already have it: $ conda install -c menpo opencv Install this repository and the dependencies using pip: $ pip install -e <root_dir> With this, you can edit the ADVENT code on the fly and imp...
Adversarial Discriminative Domain Adaptation 核心思想: 假定两个数据集很相似(特征相差不大)。 Pre-traning: 首先使用source image训练一个classifier; Adversarial Adaptation: discriminator判断from source or target.注意这里的source CNN和Pre-traning的结构参数是完全一样的;...
Adversarial-Learned Loss for Domain Adaptation By Minghao Chen, Shuai Zhao, Haifeng Liu, Deng Cai.IntroductionA PyTorch implementation for our AAAI 2020 paper "Adversarial-Learned Loss for Domain Adaptation" (ALDA). In ALDA, we use a domain discriminator to correct the noise in the pseudo-label...
This is the code release for "Partial Adversarial Domain Adaptation" (ECCV 2018) The pytorch version is in directory "pytorch". We have released the version test on PyTorch Version 0.3.1. Details of the codes are described in the README.md in "pytorch" directory. Citation If you use this...