  Data for Image segmentation models can be costly to obtain due to the
precision required by human annotators. We run a series of experiments showing
the effect of different kinds of Dropout training on the DeepLabv3+ Image
segmentation model when trained using a small dataset. We find that when
appropriate forms of Dropout are applied in the right place in the model
architecture that non-insignificant improvement in Mean Intersection over Union
(mIoU) score can be observed. In our best case, we find that applying Dropout
scheduling in conjunction with SpatialDropout improves baseline mIoU from 0.49
to 0.59. This result shows that even where a model architecture makes extensive
use of Batch Normalization, Dropout can still be an effective way of improving
performance in low data situations.
