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In this repository, we deal with the task of implementing Generative Adversarial Networks (GANs) using the CIFAR-10 dataset. Two popular GANs: DCGAN and SAGAN are implemented. The performance of the network is evaluated using the FID score.

License: MIT License

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cifar10 dcgan fid gan generative-adversarial-network keras sagan self-attention spectral-normalization tensorflow

generative-adversarial-networks-gans's Introduction

๐‡๐ž๐ฅ๐ฅ๐จ ๐Ÿ๐ž๐ฅ๐ฅ๐จ๐ฐ <๐š๐šŽ๐šŸ๐šŽ๐š•๐š˜๐š™๐šŽ๐š›๐šœ>!

Welcome!
Welcome to my GitHub profile.

I am Vineeth, currently contributing to Qualcomm as a Machine Learning Engineer.
Previously, I was a masters student in Artificial Intelligence at the Indian Institute of Science, Bengaluru.
You will likely find machine learning and data science projects along with some DSA projects here.
Feel free to clone projects, raise issues, and submit PRs if you think something could be made better.
Happy Coding!

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