APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Which of the following statements are true about Beta-VAEs? (note:beta is the coefficient of the KL term)
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When beta = 1 Beta-VAEs are equivalent to VAEs
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Increasing beta increases the constraint on the latent bottleneck
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Decreasing beta increases the constraint on the latent bottleneck
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Increasing beta increases the level of disentanglement
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Decreasing beta increases the level of disentanglement
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Explanation:
Detailed explanation-1: -Disentangled Representations This type of latent space is known as a disentangled representation. More formally, a disentangled representation maps each latent factor to a generative factor. A generative factor is simply some parameter in the process or model that generated the measurement data.
Detailed explanation-2: -We introduce now, in this post, the other major kind of deep generative models: Variational Autoencoders (VAEs). In a nutshell, a VAE is an autoencoder whose encodings distribution is regularised during the training in order to ensure that its latent space has good properties allowing us to generate some new data.
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