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Random forest and deep neural network are two schools of effectiveclassification methods in machine learning. While the random forest is robustirrespective of the data domain, the deep neural network has advantages inhandling high dimensional data. In view that a differentiable neural decisionforest can be added to the neural network to fully exploit the benefits of bothmodels, in our work, we further combine convolutional autoencoder with neuraldecision forest, where autoencoder has its advantages in finding the hiddenrepresentations of the input data. We develop a gradient boost module and embedit into the proposed convolutional autoencoder with neural decision forest toimprove the performance. The idea of gradient boost is to learn and use theresidual in the prediction. In addition, we design a structure to learn theparameters of the neural decision forest and gradient boost module atcontiguous steps. The extensive experiments on several public datasetsdemonstrate that our proposed model achieves good efficiency and predictionperformance compared with a series of baseline methods.

GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest