Understanding Visual Concepts with Continuation Learning

22 Feb 2016William F. WhitneyMichael ChangTejas KulkarniJoshua B. Tenenbaum

We introduce a neural network architecture and a learning algorithm to produce factorized symbolic representations. We propose to learn these concepts by observing consecutive frames, letting all the components of the hidden representation except a small discrete set (gating units) be predicted from the previous frame, and let the factors of variation in the next frame be represented entirely by these discrete gated units (corresponding to symbolic representations)... (read more)

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