[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

Using python and scikit-learn to make stock predictions

Athena is the only tool that you will ever need to optimize your portfolio.

Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021)

Cache Requests in Deta Bases and Echo them with Deta Micros

A Protein-RNA Interface Predictor Based on Semantics of Sequences

Official repository for "PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation"

PyTorch inference for "Progressive Growing of GANs" with CelebA snapshot

This application is the basic of automated online-class-joiner(for YıldızEdu) within the right time. Gets the ZOOM link by scheduled date and time.

Multi-Stage Episodic Control for Strategic Exploration in Text Games

Unofficial PyTorch Implementation of UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

Weak-supervised Visual Geo-localization via Attention-based Knowledge Distillation

Code for the paper "Improved Techniques for Training GANs"

Code implementation of "Sparsity Probe: Analysis tool for Deep Learning Models"

Rainbow DQN implementation that outperforms the paper's results on 40% of games using 20x less data 🌈

The goal of the exercises below is to evaluate the candidate knowledge and problem solving expertise regarding the main development focuses for the iFood ML Platform team: MLOps and Feature Store development.

Implementation of Google Brain's WaveGrad high-fidelity vocoder

An intuitive library to extract features from time series

[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss

Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms applied on Continuous Control Tasks