Sunny, I think you're right that the learning step requires accessing the whole sequence.

I had been thinking that you could use an incomplete model (as in, only use the part of the sequence you've seen already) to do real-time operations, and then update it infrequently to improve accuracy. However, in the follow-up paper that was linked to earlier, the training step took a very long time, and so it seems like the direction this work went in was away from real-time processing.

I suppose it still might work, however, if you had enough parallel resources to devote to learning with various length sequences, you could pipeline the learning steps and get refinements to your model regularly.

Also, do these techniques only deal with static backgrounds? How well would this work with a changing background - would that be no problem, just another sprite?

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