Length prediction is very hard and inconsistent.
Instead of predicting a number, it makes sense to predict a distribution from which we could sample.
# encode x to get the mu and variance parameters
x_encoded = self.encoder(x)
mu, log_var = self.fc_mu(x_encoded), self.fc_var(x_encoded)
# sample z from q
std = torch.exp(log_var / 2)
q = torch.distributions.Normal(mu, std)
z = q.rsample()
Then calculate the L1/L2 between z and the ground_truth, then propagating gradients... update weights...
In inference, just use mu, but if multiple signs, and some time limit is required (5 signs, 2 seconds), can optimally find the combination of what should be fast and slow based on the std
AmitMY
commented
2 years ago
Length prediction is very hard and inconsistent. Instead of predicting a number, it makes sense to predict a distribution from which we could sample.
Then calculate the L1/L2 between z and the ground_truth, then propagating gradients... update weights...
In inference, just use
mu, but if multiple signs, and some time limit is required (5 signs, 2 seconds), can optimally find the combination of what should be fast and slow based on thestd