[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

Last update: Dec 26, 2022

Overview

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

Prerequisites

To install requirements:

pip install -r requirements.txt

Python 3.6
GPU Memory: 10GB
Pytorch 1.4.0

Getting Started

Download the dataset: Office-31, OfficeHome, VisDA, DomainNet.

Data Folder structure:

Your dataset DIR:
|-Office/domain_adaptation_images
| |-amazon
| |-webcam
| |-dslr
|-OfficeHome
| |-Art
| |-Product
| |-...
|-VisDA
| |-train
| |-validataion
|-DomainNet
| |-clipart
| |-painting
| |-...

You need you modify the data_path in config files, i.e., config.root

Training

Train on one transfer of Office:

CUDA_VISIBLE_DEVICES=0 python office_run.py note=EXP_NAME setting=uda/osda/pda source=amazon target=dslr

To train on six transfers of Office:

CUDA_VISIBLE_DEVICES=0 python office_run.py note=EXP_NAME setting=uda/osda/pda transfer_all=1

Train on OfficeHome:

CUDA_VISIBLE_DEVICES=0 python officehome_run.py note=EXP_NAME setting=uda/osda/pda source=Art target=Product

CUDA_VISIBLE_DEVICES=0 python officehome_run.py note=EXP_NAME setting=uda/osda/pda transfer_all=1

The final results (including the best and the last) will be saved in the ./snapshot/EXP_NAME/result.txt.

Notably, transfer_all wil consumes more shared memory.

Citation

If you find it helpful, please consider citing:

@inproceedings{li2021DCC,
  title={Domain Consensus Clustering for Universal Domain Adaptation},
  author={Li, Guangrui and Kang, Guoliang and Zhu, Yi and Wei, Yunchao and Yang, Yi},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021}
}

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

Related tags

Overview

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

Prerequisites

Getting Started

Training

Citation

Owner

Guangrui Li

YKKDetector For Python

Federated Learning Based on Dynamic Regularization

Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.

Pytorch implementation of One-Shot Affordance Detection

Repository relating to the CVPR21 paper TimeLens: Event-based Video Frame Interpolation

Python-experiments - A Repository which contains python scripts to automate things and make your life easier with python

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021.

The source code of the paper "SHGNN: Structure-Aware Heterogeneous Graph Neural Network"

SemEval2022 Patronizing and Condescending Language (PCL) Detection

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

Neural Style and MSG-Net

Rethinking Nearest Neighbors for Visual Classification

Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation, NeurIPS 2021 Spotlight

Simple node deletion tool for onnx.

QA-GNN: Question Answering using Language Models and Knowledge Graphs

Use deep learning, genetic programming and other methods to predict stock and market movements

Official repository of ICCV21 paper "Viewpoint Invariant Dense Matching for Visual Geolocalization"

Source code for the BMVC-2021 paper "SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation".

PyGCL: Graph Contrastive Learning Library for PyTorch