Discussion: Beta scheduling #32
Labels
appearance
Appearance module
dynamics
Dynamics module
enhancement
New feature or request
Low Priority
The overfit test got me thinking: what if we scheduled beta? If initially beta started off at 0, or at least low like .1, then throughout training leveled out at 1. I'm thinking that this may encourage the network to first learn meaningful patterns and features in the encoder, and then ramp up the task to also having a cohesive latent space. Thoughts?
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