The L1 and L2 distance metrics can be used when training convolutional neural nets to perform nonlinear dimensionality reduction on image datasets, generating embedded spaces in a similar manner as with multidimensional scaling. The choice between them is often made arbitrarily. We trained enocder/decoder network pairs as Regressors, Autoencoders, Siamese networks, and with a triplet loss before applying them to Classification, Outlier detection, Interpolation, and Denoising. The experimental results were interpreted, subjectively where necessary, leading to the conclusion that using the euclidean or the manhattan distance during training matters less than the choice of training configuration. The L2 distance appeared minimally favorable.      «

The L1 and L2 distance metrics can be used when training convolutional neural nets to perform nonlinear dimensionality reduction on image datasets, generating embedded spaces in a similar manner as with multidimensional scaling. The choice between them is often made arbitrarily. We trained enocder/decoder network pairs as Regressors, Autoencoders, Siamese networks, and with a triplet loss before applying them to Classification, Outlier detection, Interpolation, and Denoising. The experimental re...     »