Mixing Samples Data Augmentation

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.

While Mixing Samples Data Augmentation (MSDA) offer a powerful tool for regularization, they offer very little control on the content mixed. I study how manipulating the contents mixed into the final augmented image can improve the training of networks.

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.