- 1 How do Variational Autoencoders work?
- 2 Why Variational Autoencoders?
- 3 What is the role of Kullback Leibler KL divergence in the loss function of a variational auto encoder?
- 4 Are variational Autoencoders better than Autoencoders?
- 5 What is divergence in deep learning?
- 6 When to use r2-loss or MSE-loss?
- 7 Which is better a or B in autoencoder?
- 8 When to use binary cross entropy in autoencoder?
- 9 What does it mean to optimize for MSE?
How do Variational Autoencoders work?
variational autoencoders (VAEs) are autoencoders that tackle the problem of the latent space irregularity by making the encoder return a distribution over the latent space instead of a single point and by adding in the loss function a regularisation term over that returned distribution in order to ensure a better …
Why Variational Autoencoders?
The main benefit of a variational autoencoder is that we’re capable of learning smooth latent state representations of the input data. For standard autoencoders, we simply need to learn an encoding which allows us to reproduce the input.
What is the role of Kullback Leibler KL divergence in the loss function of a variational auto encoder?
On the use of the Kullback–Leibler divergence in Variational Autoencoders. The purpose of the KL divergence term in the loss function is to make the distribution of the encoder output as close as possible to a standard multivariate normal distribution.
Are variational Autoencoders better than Autoencoders?
A variational autoencoder assumes that the source data has some sort of underlying probability distribution (such as Gaussian) and then attempts to find the parameters of the distribution. Implementing a variational autoencoder is much more challenging than implementing an autoencoder.
What is divergence in deep learning?
If you’re diving deep into deep learning or machining a better grasp of machine learning then an understanding of the Kullback-Leibler divergence can be invaluable. In layman’s terms, the K-L divergence is a measure of how different a specific probability distribution is from a reference distribution.
When to use r2-loss or MSE-loss?
When having real valued entries (e.g. floats between 0 and 1 as normalized representation for greyscale values from 0 to 256) in our label vector, I always thought that we use MSE (R2-loss) if we want to measure the distance/error between input and output or in general input and label of the network.
Which is better a or B in autoencoder?
For example, give the true value is 0.2, and autoencoder A predicts 0.1 while autoencoder 2 predicts 0.3. The loss for A would be Hence, the B is considered to be a better reconstruction than A; if I got everything correct.
When to use binary cross entropy in autoencoder?
I am working on an autoencoder for non-binary data ranging in [0,1] and while I was exploring existing solutions I noticed that many people (e.g., the keras tutorial on autoencoders, this guy) use binary cross-entropy as the loss function in this scenario.
What does it mean to optimize for MSE?
Optimizing for MSE means your generated output intensities are symmetrically close to the input intensities. A higher-than-training intensity is penalized by the same amount as an equally valued lower intensity. Cross-entropy loss is assymetrical.