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[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!!

submitted by /u/desperate_ano
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.