Why is it possible to train a semantic segmentation neural network like U-net/Tiramisu from scratch using small dataset like few hundreds.
While for the image classification task, it is not advised to train your own network from scratch even if you have thousands of images per class. I have seen, people using transfer learning (models trained on imagenet dataset )even when they have (ex. 5000 images per class for say 10 class classification) and not train the network from scratch.
One reason, I can think is, The no of parameters that need to be trained in semantic segmentation network is significantly lesser than typical image classification task. So lesser parameters helps training with lesser images.
Any other reasons?
https://www.microsoft.com/developerblog/2018/07/18/semantic-segmentation-small-data-using-keras-azure-deep-learning-virtual-machine/ : talks about semantic segmentation using small dataset.