An efficient implementation of GPNN

Last update: Apr 16, 2022

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Overview

Efficient-GPNN

An efficient implementation of GPNN as depicted in "Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models"

This is the version of GPNN I used to compare with my model in the reaserach done for writing the paper "Generating natural images with direct patch distributions matching [CVPR 2022]"

While writing this implementation I consulted the implementaion in https://github.com/iyttor/GPNN.git. My implementation offers more simplicity, a faster pytorch computiion of the NN matrix and and a low memory version of the computation done in O(N+M) as suggested in the suplementary of the paper: https://www.wisdom.weizmann.ac.il/~vision/gpnn/

Image reshuffling

# python3 scripts/reshuffle.py

Image Retargeting

# python3 scripts/retarget.py

Image style transfer

# python3 scripts/style_transfer.py

Owner

I'm an M.S.c student at the Hebrew University of Jerusalem. My current research deals with Generative Modeling of vision data

GitHub Repository

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An efficient implementation of GPNN

Related tags

Overview

Efficient-GPNN

Image reshuffling

Image Retargeting

Image style transfer

Owner

The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction".

ERISHA is a mulitilingual multispeaker expressive speech synthesis framework. It can transfer the expressivity to the speaker's voice for which no expressive speech corpus is available.

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