Figure 1: Our low-shot learning benchmark in two phases: representation learning and low-shot learning. Modern recognition models use large labeled datasets like ImageNet to build good visual representations and train strong classifiers (representation learning). However, these datasets only contain a fixed set of classes. In many realistic scenarios, once deployed, the model might encounter novel classes that it also needs to recognize, but with very few training examples available (low-shot learning). We present two ways of significantly improving performance in this scenario: (1) a novel loss function for representation learning that leads to better visual representations that generalize well, and (2) a method for hallucinating additional examples for the data-starved novel classes.  Abstract Low-shot visual learning-the ability to recognize novel object categories from very few examples-is a hallmark
REF presents a way of hallucinating additional examples for novel classes by transferring modes of variation from base classes.
Low-Shot Visual Recognition by Shrinking and Hallucinating Features
2017 IEEE International Conference on Computer Vision (ICCV)
2017 IEEE International Conference on Computer Vision (ICCV)
Computer Science