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Context: Another natural unsupervised learning task for sequences is predicting thefuture. This is the approach used in language models for modeling sequences ofwords. The design of the Future Predictor Model is same as that of theAutoencoder Model, except that the decoder LSTM in this case predicts frames ofthe video that come after the input sequence (Fig. 3).Ranzato et al. (2014) use a similar model but predict only the next frame at eachtime step. This model, on the other hand, predicts a long sequence into thefuture. Here again we can consider two variants of the decoder – conditionaland unconditioned. For each of these two models, we can consider two possibilities - one in whichthe decoder LSTM is conditioned on the last generated frame and the other inwhich it is not. In the experimental section, we explore these choicesquantitatively. Here we briefly discuss arguments for and against a conditionaldecoder. A strong argument in favour of using a conditional decoder is that itallows the decoder to model multiple modes in the target sequence distribution.Without that, we would end up averaging the multiple modes in the low-levelinput space. However, this is an issue only if we expect multiple modes in thetarget sequence distribution. For the LSTM Autoencoder, there is only onecorrect target and hence a unimodal target distribution. But for the LSTM FuturePredictor there is a possibility of multiple targets given an input because evenif we assume a deterministic universe, everything needed to predict the futurewill not necessarily be observed in the input. Future prediction results are summarized in Table 2. For MNIST we compute the cross entropy of the predictions with respect to the ground truth, both of which are 64 × 64 patches. For natural image patches, we compute the squared loss. We see that the Composite Model always does a better job of predicting the future compared to the Future Predictor. This indicates that having the autoencoder along with the future predictor to force the model to remember more about the inputs actually helps predict the future better. Next, we can compare each model with its conditional variant. Here, we find that the conditional models perform better, as was also noted in Fig. 5.

Question: Which variants of LSTM encoder-decoder models are used in this study?
Future Predictor, Composite Model, Conditional Future Predictor, Composite Model with Conditional Future Predictor are the variants of LSTM encoder-decoder models are used in this study.