Generative Adversarial Networks (GANs) have significantly advanced image
synthesis, however, the synthesis quality drops significantly given a limited
amount of training data. To improve the data efficiency of GAN training, prior
work typically employs data augmentation to mitigate the overfitting of the
discriminator yet still learn the discriminator with a bi-classification (i.e.,
real vs. fake) task. In this work, we propose a data-efficient Instance
Generation (InsGen) method based on instance discrimination. Concretely,
besides differentiating the real domain from the fake domain, the discriminator
is required to distinguish every individual image, no matter it comes from the
training set or from the generator. In this way, the discriminator can benefit
from the infinite synthesized samples for training, alleviating the overfitting
problem caused by insufficient training data. A noise perturbation strategy is
further introduced to improve its discriminative power. Meanwhile, the learned
instance discrimination capability from the discriminator is in turn exploited
to encourage the generator for diverse generation. Extensive experiments
demonstrate the effectiveness of our method on a variety of datasets and
training settings. Noticeably, on the setting of 2K training images from the
FFHQ dataset, we outperform the state-of-the-art approach with 23.5% FID
improvement.

本文提出了一种基于实例鉴别方法的数据高效生成对抗网络（GAN）训练方法，从而解决 GAN 训练中数据匮乏问题导致的过拟合问题，实验结果表明，在 FFHQ 数据集 2K 训练图像的情况下，比最先进的方法 FID 提高了 23.5％。

从实例区分中高效生成实例

Data-Efficient Instance Generation from Instance Discrimination

Deep neural networks and in particular, deep neural classifiers have become
an integral part of many modern applications. Despite their practical success,
we still have limited knowledge of how they work and the demand for such an
understanding is evergrowing. In this regard, one crucial aspect of deep neural
network classifiers that can help us deepen our knowledge about their
decision-making behavior is to investigate their decision boundaries.
Nevertheless, this is contingent upon having access to samples populating the
areas near the decision boundary. To achieve this, we propose a novel approach
we call Deep Decision boundary Instance Generation (DeepDIG). DeepDIG utilizes
a method based on adversarial example generation as an effective way of
generating samples near the decision boundary of any deep neural network model.
Then, we introduce a set of important principled characteristics that take
advantage of the generated instances near the decision boundary to provide
multifaceted understandings of deep neural networks. We have performed
extensive experiments on multiple representative datasets across various deep
neural network models and characterized their decision boundaries. The code is
publicly available at this https URL

本文提出了一种 Deep Decision boundary Instance Generation（DeepDIG）的方法，在决策边界附近通过生成样本来深入理解深度神经网络的工作机制和决策行为多方面特征。在多个数据集上进行了实验和测试。