[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

PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation

Taichi Course Homework Template

[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.

This is a Python Module For Encryption, Hashing And Other stuff

Code for the Paper "Diffusion Models for Handwriting Generation"

This repository contains the code for the paper Neural RGB-D Surface Reconstruction

VGGVox models for Speaker Identification and Verification trained on the VoxCeleb (1 & 2) datasets

This repository contains the database and code used in the paper Embedding Arithmetic for Text-driven Image Transformation

Catalyst.Detection

Simulator for FRC 2022 challenge: Rapid React

Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image", ICCV 2019

A embed able annotation tool for end to end cross document co-reference

Neural network chess engine trained on Gary Kasparov's games.

Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation

Generic Event Boundary Detection: A Benchmark for Event Segmentation

Convnext-tf - Unofficial tensorflow keras implementation of ConvNeXt

NR-GAN: Noise Robust Generative Adversarial Networks

Code repository for the work "Multi-Domain Incremental Learning for Semantic Segmentation", accepted at WACV 2022

Spectral Temporal Graph Neural Network (StemGNN in short) for Multivariate Time-series Forecasting

Mask-invariant Face Recognition through Template-level Knowledge Distillation