N-Shot Learning

N-SHOT LEARNING

1. Zero shot, one shot, few shot (siamese is one shot)

ZERO SHOT LEARNING

1. Instead of using class labels, we use some kind of vector representation for the classes, taken from a co-occurrence-after-svd or word2vec. - quite clever. This enables us to figure out if a new unseen class is near one of the known supervised classes. KNN can be used or some other distance-based classifier. Can we use word2vec for similarity measurements of new classes? Image by Dr. Timothy Hospedales, Yandex

   for classification, we can use nearest neighbour or manifold-based labeling propagation. Image by Dr. Timothy Hospedales, Yandex Multiple category vectors? Multilabel zero-shot also in the video

2. with siamese networks

GPT3 is ZERO, ONE, FEW

1. Prompt Engineering Tips & Tricks

2. Open GPT3 prompt engineering