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Context: Generalization over time scalesIn the next experiment, we test if the model can work at time scales that aredifferent than what it was trained on. We take a one hidden layer unconditionedComposite Model trained on moving MNIST digits. The model has 2048 LSTM unitsand looks at a 64 \times 64 input. It was trained on input sequences of 10frames to reconstruct those 10 frames as well as predict 10 frames into thefuture. In order to test if the future predictor is able to generalize beyond 10frames, we let the model run for 100 steps into the future.Fig. 7(a) shows the pattern of activity in the LSTM units of thefuture predictorpathway for a randomly chosen test input. It shows the activity at each of thethree sigmoidal gates (input, forget, output), the input (after the tanhnon-linearity, before being multiplied by the input gate), the cell state andthe final output (after being multiplied by the output gate). Even though theunits are ordered randomly along the vertical axis, we can see that the dynamicshas a periodic quality to it. The model is able to generate persistent motionfor long periods of time. In terms of reconstruction, the model only outputsblobs after the first 15 frames, but the motion is relatively well preserved.More results, including long range future predictions over hundreds of time steps can see been athttp://www.cs.toronto.edu/~nitish/unsupervised_video.To show that setting up a periodic behaviour is not trivial,Fig. 7(b) shows the activity from a randomly initialized futurepredictor. Here, the LSTM state quickly converges and the outputs blur completely. Experiments on MNIST; We first trained our models on a dataset of moving MNIST digits. In this dataset, each video was 20 frames long and consisted of two digits moving inside a 64 × 64 patch. The digits were chosen randomly from the training set and placed initially at random locations inside the patch. Experiments on Natural Image Patches; Next, we tried to see if our models can also work with natural image patches. For this, we trained the models on sequences of 32 × 32 natural image patches extracted from the UCF-101 dataset. In this case, we used linear output units and the squared error loss function. Out-of-domain Inputs; Next, we test this model’s ability to deal with out-of domain inputs. For this, we test the model on sequences of one and three moving digits. The model was trained on sequences of two moving digits, so it has never seen inputs with just one digit or three digits. Visualizing Features; Next, we visualize the features learned by this model. Fig. 9 shows the weights that connect each input frame to the encoder LSTM. There are four sets of weights. One set of weights connects the frame to the input units. There are three other sets, one corresponding to each of the three gates (input, forget and output). Each weight has a size of 64 × 64.

Question: How many different types of experiments are performed to test the proposed models?
5 different types of experiments are performed to test the proposed models. They are Generalization over time scales, Experiments on MNIST, Experiments on Natural Image Patches, Out-of-domain Inputs, and Visualizing Features.