Abstract
In this paper, we are interested in the few-shot learning
problem. In particular, we focus on a challenging scenario
where the number of categories is large and the number of
examples per novel category is very limited, e.g. 1, 2, or 3.
Motivated by the close relationship between the parameters
and the activations in a neural network associated with the
same category, we propose a novel method that can adapt
a pre-trained neural network to novel categories by directly
predicting the parameters from the activations. Zero training is required in adaptation to novel categories, and fast
inference is realized by a single forward pass. We evaluate
our method by doing few-shot image recognition on the ImageNet dataset, which achieves the state-of-the-art classifi-
cation accuracy on novel categories by a significant margin
while keeping comparable performance on the large-scale
categories. We also test our method on the MiniImageNet
dataset and it strongly outperforms the previous state-ofthe-art methods