Summary
- we model the meta-training set as the combination of all the individual task specific training sets instead of a multi-task setting.
- We understand the cross-domain connection stored in the feature extractor using our Memory Augmented Propagation network which stores the information from the previous layers of our backbone.
- We apply self-attention on each output feature map of the layers of our backbone in a hierarchical manner to find co-dependency.
- We test on the popular CIFAR-FS and miniImageNet datasets and find that our results are at par and sometimes even better than convolutional based SOTA approaches like MetaOptNet.
