Contents
What is the purpose of the KL divergence in the loss function?
If you don’t know what is a VAE, you could start by giving a look at that introduction. 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.
Why do we adjust the weight of the KL term in VAE?
Intuitively, during the course of VAE training, we periodically adjust the weight of the KL term in the objective function, providing the model opportunities to learn to leverage the global latent variables in text generation, thus encoding as much global information in the latent variables as possible.
What is the KL term for naive VAE training?
Recent work has found that naïve training of VAEs (keeping constant β=1) leads to model degeneration—the KL term becomes vanishingly small.
Which is the best method for VAE training?
On the Penn Tree-Bank dataset, the cyclical schedule can provide more informative language representations (measured by the improved KL term), while retaining the similar perplexity. It is significantly faster than existing methods, and can be combined to improve upon them.
When do you need to weight the KL divergence?
Therefore, if the weight factor of MSE term (or, E D ( w) in this case) is 1, we need to weight the KL divergence with a factor β K L = 1 / β to be mathematically correct. In practice, people often find a good value of the precision β K L through hyper-parameter tuning.
How to weight KLD loss in machine learning?
However, the loss in VAE consists of the NLL (or reconstruction loss) and the regularization (KL loss). Therefore, if the weight factor of MSE term (or, E D ( w) in this case) is 1, we need to weight the KL divergence with a factor β K L = 1 / β to be mathematically correct.
Is the loss in VAE the same as the NLL?
This might be fine for conventional regression where the loss consists of only the negative log-likelihood (NLL) − ln p ( t | x, w, β), and the prediction would be the mean of the target variable t. However, the loss in VAE consists of the NLL (or reconstruction loss) and the regularization (KL loss).