https://github.com/abhaskumarsinha/1d-gan

A simple well-documented tutorial on implementing a 1D GAN on Keras using a Python Jupyter Notebook
https://github.com/abhaskumarsinha/1d-gan

deep-learning deep-neural-networks gan generative-adversarial-network keras keras-tensorflow python3 tensorflow2 tutorial

Last synced: about 1 month ago
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A simple well-documented tutorial on implementing a 1D GAN on Keras using a Python Jupyter Notebook

• Host: GitHub
• URL: https://github.com/abhaskumarsinha/1d-gan
• Owner: abhaskumarsinha
• License: mit
• Created: 2022-07-21T20:18:45.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-07-22T18:44:35.000Z (almost 4 years ago)
• Last Synced: 2025-03-15T05:42:31.770Z (over 1 year ago)
• Topics: deep-learning, deep-neural-networks, gan, generative-adversarial-network, keras, keras-tensorflow, python3, tensorflow2, tutorial
• Language: Jupyter Notebook
• Homepage:
• Size: 120 KB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 1D-GAN

A simple well-documented tutorial on implementing a 1D GAN on Keras using a Python Jupyter Notebook

Refer 1D-GAN.ipynb notebook. You may run it on Google Colab too.

Refer DC-GAN for a GAN that generates image data.

## Output Progress

https://user-images.githubusercontent.com/31654395/180387174-e93db33e-e192-4f13-b56d-b4252bde6777.mp4

![1](https://github.com/abhaskumarsinha/1D-GAN/raw/main/sample-outputs/1.png)

![2](https://github.com/abhaskumarsinha/1D-GAN/raw/main/sample-outputs/2.png)

![3](https://github.com/abhaskumarsinha/1D-GAN/raw/main/sample-outputs/3.png)

![4](https://github.com/abhaskumarsinha/1D-GAN/raw/main/sample-outputs/4.png)

![5](https://github.com/abhaskumarsinha/1D-GAN/raw/main/sample-outputs/5.png)

![6](https://github.com/abhaskumarsinha/1D-GAN/raw/main/sample-outputs/6.png)

![7](https://github.com/abhaskumarsinha/1D-GAN/raw/main/sample-outputs/7.png)

# **BIBLIOGRAPHY**

- I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems, 2014, pp. 2672–2680.

- A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” in Proceedings of the 5th International Conference on Learning Representations (ICLR) - workshop track, 2016.

- Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

- I. J. Goodfellow, “On distinguishability criteria for estimating generative models,” International Conference on Learning Representations - workshop track, 2015

- I. Goodfellow, “Nips 2016 tutorial: Generative adversarial networks,” 2016, presented at the Neural Information Processing Systems Conference. [Online]. Available: https://arxiv.org/abs/1701.00160

- J. D. Lee, M. Simchowitz, M. I. Jordan, and B. Recht, “Gradient descent only converges to minimizers,” in Conference on Learning Theory, 2016, pp. 1246–1257

- Thanh-Tung, Hoang, and Truyen Tran. "Catastrophic forgetting and mode collapse in gans." 2020 international joint conference on neural networks (ijcnn). IEEE, 2020.

- Kushwaha, Vandana, and G. C. Nandi. "Study of prevention of mode collapse in generative adversarial network (GAN)." 2020 IEEE 4th Conference on Information & Communication Technology (CICT). IEEE, 2020.