Domain Adaptation with Invariant RepresentationLearning: What Transformations to Learn?

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Deep LearningDSAN
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
Owner
DMIRLAB
DMIRLAB Public Repository
DMIRLAB
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