[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

Overview

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

[Paper]

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

or

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}
}

[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Large Scale Learning on Non-Homophilous Graphs: New Benchmark

60 Jan 03, 2023
Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

Aesara 898 Jan 07, 2023
MLP-Numpy - A simple modular implementation of Multi Layer Perceptron in pure Numpy.

MLP-Numpy A simple modular implementation of Multi Layer Perceptron in pure Numpy. I used the Iris dataset from scikit-learn library for the experimen

Soroush Omranpour 1 Jan 01, 2022
A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm

Multi-Agent-Deep-Deterministic-Policy-Gradients A Pytorch implementation of the multi agent deep deterministic policy gradients(MADDPG) algorithm This

Phil Tabor 159 Dec 28, 2022
PASSL包含 SimCLR,MoCo,BYOL,CLIP等基于对比学习的图像自监督算法以及 Vision-Transformer,Swin-Transformer,BEiT,CVT,T2T,MLP_Mixer等视觉Transformer算法

PASSL Introduction PASSL is a Paddle based vision library for state-of-the-art Self-Supervised Learning research with PaddlePaddle. PASSL aims to acce

186 Dec 29, 2022
Basics of 2D and 3D Human Pose Estimation.

Human Pose Estimation 101 If you want a slightly more rigorous tutorial and understand the basics of Human Pose Estimation and how the field has evolv

Sudharshan Chandra Babu 293 Dec 14, 2022
:hot_pepper: R²SQL: "Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing." (AAAI 2021)

R²SQL The PyTorch implementation of paper Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing. (AAAI 2021) Requirement

huybery 60 Dec 31, 2022
Source code for "OmniPhotos: Casual 360° VR Photography"

OmniPhotos: Casual 360° VR Photography Project Page | Video | Paper | Demo | Data This repository contains the source code for creating and viewing Om

Christian Richardt 144 Dec 30, 2022
PyTorch implementation for paper "Full-Body Visual Self-Modeling of Robot Morphologies".

Full-Body Visual Self-Modeling of Robot Morphologies Boyuan Chen, Robert Kwiatkowskig, Carl Vondrick, Hod Lipson Columbia University Project Website |

Boyuan Chen 32 Jan 02, 2023
Download from Onlyfans.com.

OnlySave: Onlyfans downloader Getting Started: Download the setup executable from the latest release. Install and run. Only works on Windows currently

4 May 30, 2022
A Pytorch Implementation of ClariNet

ClariNet A Pytorch Implementation of ClariNet (Mel Spectrogram -- Waveform) Requirements PyTorch 0.4.1 & python 3.6 & Librosa Examples Step 1. Downlo

Sungwon Kim 286 Sep 15, 2022
Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network Paddle-PANet 目录 结果对比 论文介绍 快速安装 结果对比 CTW1500 Method Backbone Fine

7 Aug 08, 2022
M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images

M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images This repo is the official implementation of paper "M2MRF: Man

12 Dec 14, 2022
Official git for "CTAB-GAN: Effective Table Data Synthesizing"

CTAB-GAN This is the official git paper CTAB-GAN: Effective Table Data Synthesizing. The paper is published on Asian Conference on Machine Learning (A

30 Dec 26, 2022
Fashion Entity Classification

Fashion-Entity-Classification - Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grays

ADITYA SHAH 1 Jan 04, 2022
FaRL for Facial Representation Learning

FaRL for Facial Representation Learning This repo hosts official implementation of our paper General Facial Representation Learning in a Visual-Lingui

Microsoft 19 Jan 05, 2022
*ObjDetApp* deploys a pytorch model for object detection

*ObjDetApp* deploys a pytorch model for object detection

Will Chao 1 Dec 26, 2021
Language model Prompt And Query Archive

LPAQA: Language model Prompt And Query Archive This repository contains data and code for the paper How Can We Know What Language Models Know? Install

127 Dec 20, 2022
Code for ACL 2019 Paper: "COMET: Commonsense Transformers for Automatic Knowledge Graph Construction"

To run a generation experiment (either conceptnet or atomic), follow these instructions: First Steps First clone, the repo: git clone https://github.c

Antoine Bosselut 575 Jan 01, 2023
ML powered analytics engine for outlier detection and root cause analysis.

Website • Docs • Blog • LinkedIn • Community Slack ML powered analytics engine for outlier detection and root cause analysis ✨ What is Chaos Genius? C

Chaos Genius 523 Jan 04, 2023