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LATENT SPACES (Part-3): A Practical Introduction to Deep Convolutional Generative Adversarial Network (DCGAN)

In the previous tutorial (https://medium.com/mlearning-ai/latent-spaces-part-2-a-simple-guide-to-variational-autoencoders-9369b9abd6f) we learned about variational autoencoders and their implementation in TensorFlow. In this tutorial, we shall explore another deep learning architecture called Generative Adversarial Network (GAN).

Figure 1: An example output of a DCGAN trained on Bicycles Dataset

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J. Rafid Siddiqui, PhD

J. Rafid Siddiqui, PhD

AI/ML/CV Researcher, Writer and Innovator. Talks about topics in Philosophy, Computer Vision, Machine Learning, Deep learning and AI.

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