Discriminative restricted Boltzmann machine for invariant pattern recognition with linear transformations ?
A novel model TIClassRBM is proposed to achieve invariant pattern recognition. Three types of transformation in images are incorporated into ClassRBM. Probabilistic max-pooling is adopted to learn invariant representation. Label vector is connected to the units that extract invariant representations. Experiments on MNIST and NORB datasets show the effectiveness of TIClassRBM.
discriminative restricted boltzmann machine invariant pattern recognition linear transformation probabilistic max-pooling transformation invariance