  The advent of Industry 4.0 has precipitated the incorporation of Artificial
Intelligence (AI) methods within industrial contexts, aiming to realize
intelligent manufacturing, operation as well as maintenance, also known as
industrial intelligence. However, intricate industrial milieus, particularly
those relating to energy exploration and production, frequently encompass data
characterized by long-tailed class distribution, sample imbalance, and domain
shift. These attributes pose noteworthy challenges to data-centric Deep
Learning (DL) techniques, crucial for the realization of industrial
intelligence. The present study centers on the intricate and distinctive
industrial scenarios of Nuclear Power Generation (NPG), meticulously
scrutinizing the application of DL techniques under the constraints of finite
data samples. Initially, the paper expounds on potential employment scenarios
for AI across the full life-cycle of NPG. Subsequently, we delve into an
evaluative exposition of DL's advancement, grounded in the finite sample
perspective. This encompasses aspects such as small-sample learning, few-shot
learning, zero-shot learning, and open-set recognition, also referring to the
unique data characteristics of NPG. The paper then proceeds to present two
specific case studies. The first revolves around the automatic recognition of
zirconium alloy metallography, while the second pertains to open-set
recognition for signal diagnosis of machinery sensors. These cases, spanning
the entirety of NPG's life-cycle, are accompanied by constructive outcomes and
insightful deliberations. By exploring and applying DL methodologies within the
constraints of finite sample availability, this paper not only furnishes a
robust technical foundation but also introduces a fresh perspective toward the
secure and efficient advancement and exploitation of this advanced energy
source.
