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Video prediction models often combine three components: an encoder from pixelspace to a small latent space, a latent space prediction model, and agenerative model back to pixel space. However, the large and unpredictablepixel space makes training such models difficult, requiring many trainingexamples. We argue that finding a predictive latent variable and using it toevaluate the consistency of a future image enables data-efficient predictionsbecause it precludes the necessity of a generative model training. Todemonstrate it, we created sequence completion intelligence tests in which thetask is to identify a predictably changing feature in a sequence of images anduse this prediction to select the subsequent image. We show that aone-dimensional Markov Contrastive Predictive Coding (M-CPC_1D) model solvesthese tests efficiently, with only five examples. Finally, we demonstrate theusefulness of M-CPC_1D in solving two tasks without prior training: anomalydetection and stochastic movement video prediction.

Zero-Episode Few-Shot Contrastive Predictive Coding: Solving intelligence tests without prior training