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SSD Net: toward deep network models based on dissimilarity metrics

Most artificial neural networks that originate from the perceptron use the inner product as the basic operation to calculate pattern similarities. Unlike them, we propose a novel hierarchical network model based on a pattern dissimilarity operation using a popular dissimilarity metric: sum of squared differences. Our model is differentiable and end-to-end trainable. We provide a description of the basic formulation and network architecture of the proposed method. Then we apply our method to image classification tasks using public datasets for performance comparison. Although our method does not outperform the same size of convolutional neural network in terms of classification accuracy, it demonstrates that comparable performance can be obtained.