Multi Input Multi Output

Multi-Input Multi-Output training leads to the emergence of smaller subnetworks within the base network. This has a number of benefits such a better utilization of network parameters, better training and strong regularization of learned features.

Mixing Samples Data Augmentation (MSDA) help regularize models by mixing the contents of multiple images during training. I am interested in how these methods can help the models learn better and more general features by contrasting the contents of the original mixed inputs.