Now we have a high-level understanding of what generative networks entail, we can focus on a specific type of generative models. One of them is the VAE, proposed by both Kingma and Welling (2013) as well as Rezende, Mohamed, and Wierstra (2014). This model is actually very similar to the autoencoders we saw in the last chapter, but they come with a slight twist—well, several twists, to be more specific. For one, the latent space being learned is no longer a discrete one, but a continuous one by design! So, what's the big deal? Well, as we explained earlier, we will be sampling from this latent space to generate our outputs. However, sampling from a discrete latent space is problematic. The fact that it is discrete implies that there will be regions in the latent space with discontinuities, meaning that if these regions were to be randomly sampled...
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Niloy Purkait is a technology and strategy consultant by profession. He currently resides in the Netherlands, where he offers his consulting services to local and international companies alike. He specializes in integrated solutions involving artificial intelligence, and takes pride in navigating his clients through dynamic and disruptive business environments. He has a masters in Strategic Management from Tilburg University, and a full specialization in data science from Michigan University. He has advanced industry grade certifications from IBM, in subjects like signal processing, cloud computing, machine and deep learning. He is also perusing advanced academic degrees in several related fields, and is a self-proclaimed lifelong learner.
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Niloy Purkait is a technology and strategy consultant by profession. He currently resides in the Netherlands, where he offers his consulting services to local and international companies alike. He specializes in integrated solutions involving artificial intelligence, and takes pride in navigating his clients through dynamic and disruptive business environments. He has a masters in Strategic Management from Tilburg University, and a full specialization in data science from Michigan University. He has advanced industry grade certifications from IBM, in subjects like signal processing, cloud computing, machine and deep learning. He is also perusing advanced academic degrees in several related fields, and is a self-proclaimed lifelong learner.
Read more about Niloy Purkait