ZUNIT - Toward Zero-Shot Unsupervised Image-to-Image Translation

Last update: Jun 24, 2022

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Overview

ZUNIT

Dependencies

you can install all the dependencies by

pip install -r requirements.txt

Datasets

Download CUB dataset.
Unzip the birds.zip at ./dataset.

Training

To view training results and loss plots, run

python -m visdom.server -p 8080

and click the URL http://localhost:8080.

bash ./scripts/train_bird.sh

Testing

bash ./scripts/test_bird.sh

The testing results will be saved in checkpoints/{exp_name}/results directory.

Results

Acknowledgement

The code used in this research is inspired by DMIT and FUNIT.

Contact

Feel free to contact me if there is any questions ([email protected]).

Owner

Chen Yuanqi

GitHub Repository

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ZUNIT - Toward Zero-Shot Unsupervised Image-to-Image Translation

Related tags

Overview

ZUNIT

Dependencies

Datasets

Training

Testing

Results

Acknowledgement

Contact

Owner

Chen Yuanqi

hashily is a Python module that provides a variety of text decoding and encoding operations.

RuCLIP-SB (Russian Contrastive Language–Image Pretraining SWIN-BERT) is a multimodal model for obtaining images and text similarities and rearranging captions and pictures. Unlike other versions of the model we use BERT for text encoder and SWIN transformer for image encoder.

A framework for training and evaluating AI models on a variety of openly available dialogue datasets.

Code for the paper "Are Sixteen Heads Really Better than One?"

Implementaion of our ACL 2022 paper Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation

A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks

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