  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 classification
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-of-the-art
methods.
