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Deconvolutional Networks - matthewzeiler

Deconvolutional Networks Matthew D. Zeiler, Dilip Krishnan, Graham W. Taylor and Rob Fergus Dept. of Computer Science, Courant Institute, New York University Abstract (a). Building robust low and mid-level image representa- tions, beyond edge primitives, is a long-standing goal in vision. Many existing feature detectors spatially pool edge (b). information which destroys cues such as edge intersections, parallelism and symmetry. We present a learning frame- work where features that capture these mid-level cues spon- Figure 1. (a): Tokens from Fig.

Our proposed model is similar in spirit to the Convo-lutional Networks of LeCun et al. [13], but quite different in operation. Convolutional networks are a bottom-up (a) (b) Figure 1. (a): “Tokens” from Fig. 2-4 of Vision by D. Marr [18]. These idealized local groupings are proposed as an intermediate

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