Visual object classification by sparse convolutional neural networks
Alexander Gepperth · The European Symposium on Artificial Neural Networks · 2006
A convolutional network architecture termed sparse convolu- tional neural network (SCNN) is proposed and tested on a real-world clas- sification task (car classification). In addition to the error function based on the mean squared error (MSE), approximate decorrelation between hid- den layer neurons is enforced by a weight orthogonalization mechanism. The aim is to obtain a sparse coding of the objects' visual appearance, thus removing the need for a dedicated feature selection stage. Working on un- processed image data only, it is demonstrated that classification accuracies can be improved by the proposed method compared to purely MSE-trained SCNNs and fully-connected multilayer perceptron architectures.