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Benefitting from the orthogonal properties of Kasami codes, and considering that latent representations generated by the network for a data point that belongs to more than one class can b approximated as the sum of the individual latent representations learned during training for all the classes present in this multi-label data point, we trained neural networks only on single label images then tested it with multi-label images without additional multi-label training.