Highlight: In this post, we will be discussing Variational Autoencoders (VAE). In order to fully understand the underlying ideas, we need to have a basic understanding of traditional Autoencoders. Luckily, we have already written about them in our previous posts. This post will consist of several topics. First, we will review autoencoders. Then, we will give some review of basic probability concepts. Next, we will explain what Kullback Leibler divergence is. In addition, we will…
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Highlight: Over the past few years in machine learning we’ve seen dramatic progress in the field of generative models. While there are a lot of different flavors of these generative models in this post we want to talk specifically about one model called the Generative Adversarial Network or in short GAN. Have you ever wanted to see what you would look like as part of the opposite gender? Or what about playing with the age…
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Highlights: GANs and classical Deep Learning methods (classification, object detection) are similar, but they are also fundamentally different in nature. Reviewing their properties will be the topic of this post. Therefore, before we proceed further with the GANs series, it will be useful to refresh and recap what is supervised and unsupervised learning. In addition, we will explain the difference between discriminative and generative models. Finally, we will introduce latent variables, since they are an…
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