$ 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...
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 Office-31 The list files of Office-31 dataset are...
Code release for"Conditional Adversarial Domain Adaptation"(NIPS 2018) New version:https://github.com/thuml/Transfer-Learning-Library Dataset Digits Processed SVHN_dataset ishere. We change the original mat into images. Other transformed images are indata/svhn2mnistanddata/usps2mnist. Dataset_train...
3Branches0Tags Folders and files Name Last commit message Last commit date Latest commit cuishuhao Update README.md Mar 31, 2020 d2a7ea6·Mar 31, 2020 History 5 Commits CDAN-GD initial Mar 19, 2020 GVB-GD update Mar 13, 2020
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; Testing:对target image进行预测label。 数据集说明: 主要是USTP(target)和MNIST(source). import pickle import gzip import os url = "https://raw.githubusercontent.com/mingyuliutw/CoG...
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最小化的,这在实践中很难实现。一些方法通过显式地建模表示...
May 19, 2023 LICENSE Initial commit Aug 10, 2021 README.md Update README.md May 19, 2023 DADA-AAAI2020 Code release for Discriminative Adversarial Domain Adaptation (AAAI2020). Project Page⋅PDF Download The code has been transferred to our organization website. Clickhereto go to the target...
Domain adaptation The goal of domain adaptation is to transfer the knowledge of a model to a different but related data distribution. The model is trained on asourcedataset and applied to atargetdataset (usually unlabeled). In this case, the model is trained on regular MNIST images, but we ...
Domain adaptation is an effective method solving the learning tasks lack of labeled data. In recent years, the adversarial domain adaptation (ADA) has achieved attractive results in a series transfer learning tasks. ADA reduces the distribution discrepancy between the source and the target by ...
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...