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RockySJ avatar

RockySJ/WDGRL

0
View on GitHub↗
130 stars·35 forks·Python·8 views

WDGRL

Tensorflow version: 1.3.0

Features

  • Adversarial Adaptation Methods - Wasserstein distance guided representation learning for adaptation.

Star history

Star history chart for rockysj/wdgrlStar history chart for rockysj/wdgrl

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Frequently asked questions

What does rockysj/wdgrl do?

Tensorflow version: 1.3.0

What are the main features of rockysj/wdgrl?

The main features of rockysj/wdgrl are: Adversarial Adaptation Methods.

Which projects share features with rockysj/wdgrl?

Projects with overlapping indexed features include: thuml/transfer-learning-library — This project is a comprehensive library for transfer learning and domain adaptation in computer vision. It serves as a… cuishuhao/gvb — Code of Gradually Vanishing Bridge for Adversarial Domain Adaptation (CVPR2020). ddtm/caffe — Caffe: a fast open framework for deep learning. dmirlab-group/dsr — The implement of "Learning Disentangled Semantic Representation for Domain Adaptation" (IJCAI 2019). engharat/sbadagan — SBADA-GAN CVPR 2018 code This is a preliminary release, as the code needs a massive cleanup being extremely verbose in… crownx/spa — Official implementation for SPA: A Graph Spectral Alignment Perspective for Domain Adaptation (NeurIPS 2023).

Projects sharing features with WDGRL

These projects share indexed features with WDGRL. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • thuml/transfer-learning-librarythuml avatar

    thuml/Transfer-Learning-Library

    3,917View on GitHub↗

    This project is a comprehensive library for transfer learning and domain adaptation in computer vision. It serves as a framework for aligning feature distributions between source and target datasets, a toolkit for domain generalization, and a library for semi-supervised learning using small labeled datasets and large unlabeled sets. The library provides specialized capabilities for unsupervised domain adaptation, including the use of adversarial networks, discrepancy-based architectures, and image-to-image translation to reduce distribution mismatch. It also includes tools for domain generali

    Python
    View on GitHub↗3,917
  • cuishuhao/gvbcuishuhao avatar

    cuishuhao/GVB

    82View on GitHub↗

    Code of Gradually Vanishing Bridge for Adversarial Domain Adaptation (CVPR2020)

    Python
    View on GitHub↗82
  • ddtm/caffeddtm avatar

    ddtm/caffe

    137View on GitHub↗

    Caffe: a fast open framework for deep learning.

    C++
    View on GitHub↗137
  • crownx/spaCrownX avatar

    CrownX/SPA

    18View on GitHub↗

    Official implementation for SPA: A Graph Spectral Alignment Perspective for Domain Adaptation (NeurIPS 2023)

    Python
    View on GitHub↗18
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