Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation

no code implementations • 6 Jun 2014 • Paul A. Szerlip, Gregory Morse, Justin K. Pugh, Kenneth O. Stanley

Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called divergent discriminative feature accumulation (DDFA) that instead continually accumulates features that make novel discriminations among the training set.

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