  This paper introduces and studies zero-base generalized few-shot learning
(zero-base GFSL), which is an extreme yet practical version of few-shot
learning problem. Motivated by the cases where base data is not available due
to privacy or ethical issues, the goal of zero-base GFSL is to newly
incorporate the knowledge of few samples of novel classes into a pretrained
model without any samples of base classes. According to our analysis, we
discover the fact that both mean and variance of the weight distribution of
novel classes are not properly established, compared to those of base classes.
The existing GFSL methods attempt to make the weight norms balanced, which we
find helps only the variance part, but discard the importance of mean of
weights particularly for novel classes, leading to the limited performance in
the GFSL problem even with base data. In this paper, we overcome this
limitation by proposing a simple yet effective normalization method that can
effectively control both mean and variance of the weight distribution of novel
classes without using any base samples and thereby achieve a satisfactory
performance on both novel and base classes. Our experimental results somewhat
surprisingly show that the proposed zero-base GFSL method that does not utilize
any base samples even outperforms the existing GFSL methods that make the best
use of base data. Our implementation is available at:
https://github.com/bigdata-inha/Zero-Base-GFSL.
