[D] use LSTM to predict feature from a pretrained network
Hey, I am currently working on the sequential feature prediction using LSTM (the sequential feature can be extracted by a pretrained VGG or the latent space from an autoencoder), so basically the extracted feature is considered as “ground truth”, and I am using LSTM to predict the future features.
The feature space is always not constrained to a specific interval, sometimes the values vary between [-10,50], but the tanh activation layer in LSTM will constrain the output between [-1,1] (If I understand correct?). I have also played with adding fc layer with leakyrelu or deconv layers after the LSTM layer, but the output is still in a much smaller interval, so the scale mismatch between my ground truth feature and predicted latent space make it really hard to train this model. I am wondering has anyone faced this issue before? And do you have some ideas about how to solve it?
Thanks in advance!!