Domain Adaptation with Invariant RepresentationLearning: What Transformations to Learn?

Related tags

Deep Learning DSAN
Overview

Domain Adaptation with Invariant RepresentationLearning: What Transformations to Learn?

Repository Structure:

DSAN
|└───amazon
|    └── dataset (Amazon dataset)
|    ├── result
|    ├── amazon_utils.py
|    ├── dsan.py
|    └── flip_gradient.py
|    └── logger.py
|────imageclef
|    └── dataset (ImageCLEF dataset)
|    ├── logs
|    ├── utils.py
|    ├── dsan.py
|    └── flip_gradient.py

Instructions on running the code: ##1. Run the following command



# for Amazon
cd amazon
python dsan.py --src $source_domain_name --tgt $target_domain_name 

# for ImageCLEF
cd imageclef
python dsan.py --src $source_domain_name --tgt $target_domain_name

##2. Compute environment for our experiments:
CPU: Intel 7700k
GPU: GeForce RTX2070
32 GB Memory
##3. Table of the experiment result for Amazon:

Model B→D B→E B→K D→B D→E D→K E→D E→B E→K K→B K→D K→E
DIRT-T 78.6 76.1 75.5 76.8 75.2 79.1 69.6 71.0 84.2 69.2 73.3 79.5
MDD 77.1 74.4 77.0 74.7 74.1 76.3 72.4 70.2 83.3 69.3 73.2 82.8
DSAN 82.7 80.8 82.6 79.5 81.4 85.3 76.7 75.1 88.0 73.8 77.3 85.0
You might also like...
Code release for
Code release for "Transferable Semantic Augmentation for Domain Adaptation" (CVPR 2021)

Transferable Semantic Augmentation for Domain Adaptation Code release for "Transferable Semantic Augmentation for Domain Adaptation" (CVPR 2021) Paper

null 66 Dec 16, 2022
Code to reproduce the experiments in the paper
Code to reproduce the experiments in the paper "Transformer Based Multi-Source Domain Adaptation" (EMNLP 2020)

Transformer Based Multi-Source Domain Adaptation Dustin Wright and Isabelle Augenstein To appear in EMNLP 2020. Read the preprint: https://arxiv.org/a

CopeNLU 36 Dec 5, 2022
PyTorch code for the paper
PyTorch code for the paper "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" (CVPR2021)

PyTorch code for the paper "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" (CVPR2021) This repo presents PyTorch implementation of M

Evgeny 79 Dec 19, 2022
Self-Supervised Learning for Domain Adaptation on Point-Clouds
Self-Supervised Learning for Domain Adaptation on Point-Clouds

Self-Supervised Learning for Domain Adaptation on Point-Clouds Introduction Self-supervised learning (SSL) allows to learn useful representations from

Idan Achituve 66 Dec 20, 2022
code for our paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"

SHOT++ Code for our TPAMI submission "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer" that is ext

null 75 Dec 16, 2022
The official codes of
The official codes of "Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners".

SSL models are Strong UDA learners Introduction This is the official code of paper "Semi-supervised Models are Strong Unsupervised Domain Adaptation L

Yabin Zhang 26 Dec 26, 2022
Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”
Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”

Official implementation for TransDA Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”. Overview: Result: Prerequisites:

stanley 54 Dec 22, 2022
A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation(DANN), support Office-31 and Office-Home dataset

DANN A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation Prerequisites Linux or OSX NVIDIA GPU + CUDA (may CuDNN) and corre

null 8 Apr 16, 2022
(CVPR2021) DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation
(CVPR2021) DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation CVPR2021(oral) [arxiv] Requirements python3.7 pytorch==

W-zx-Y 85 Dec 7, 2022
Comments
  • A question about vat_loss

    A question about vat_loss

    Thank you for your sharing this project. I read the paper and watched the code. But I was still confused about vat_loss.
    /imageclef/dsan.py line153

    What is the effection of this function? Is this function have the correspondence in the paper?

    Thank you so much.

    opened by lu77777777 1
Owner
DMIRLAB
DMIRLAB Public Repository
DMIRLAB
GitHub
Code for CVPR2021 "Visualizing Adapted Knowledge in Domain Transfer". Visualization for domain adaptation. #explainable-ai

Visualizing Adapted Knowledge in Domain Transfer @inproceedings{hou2021visualizing, title={Visualizing Adapted Knowledge in Domain Transfer}, auth

Yunzhong Hou 80 Dec 25, 2022
[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation [Paper] Prerequisites To install requirements: pip install -r requirements.txt

Guangrui Li 84 Dec 26, 2022
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation [arxiv] This is the official repository for CDTrans: Cross-domain Transformer for

null 238 Dec 22, 2022
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

[ICCV2021] TransReID: Transformer-based Object Re-Identification [pdf] The official repository for TransReID: Transformer-based Object Re-Identificati

DamoCV 569 Dec 30, 2022
A Pytorch Implementation of [Source data‐free domain adaptation of object detector through domain

A Pytorch Implementation of Source data‐free domain adaptation of object detector through domain‐specific perturbation Please follow Faster R-CNN and

null 1 Dec 25, 2021
We present a framework for training multi-modal deep learning models on unlabelled video data by forcing the network to learn invariances to transformations applied to both the audio and video streams.

Multi-Modal Self-Supervision using GDT and StiCa This is an official pytorch implementation of papers: Multi-modal Self-Supervision from Generalized D

Facebook Research 42 Dec 9, 2022
DIRL: Domain-Invariant Representation Learning

DIRL: Domain-Invariant Representation Learning Domain-Invariant Representation Learning (DIRL) is a novel algorithm that semantically aligns both the

Ajay Tanwani 30 Nov 7, 2022
This is an example implementation of the paper "Cross Domain Robot Imitation with Invariant Representation".

IR-GAIL This is an example implementation of the paper "Cross Domain Robot Imitation with Invariant Representation". Dependency The experiments are de

Zhao-Heng Yin 1 Jul 14, 2022
Learning Domain Invariant Representations in Goal-conditioned Block MDPs

Learning Domain Invariant Representations in Goal-conditioned Block MDPs Beining Han, Chongyi Zheng, Harris Chan, Keiran Paster, Michael R. Zhang, Jim

Chongyi Zheng 3 Apr 12, 2022
Progressive Domain Adaptation for Object Detection

Progressive Domain Adaptation for Object Detection Implementation of our paper Progressive Domain Adaptation for Object Detection, based on pytorch-fa

null 96 Nov 25, 2022