With the proliferation of ever more complicated Deep Learning architectures,
data synthesis is a highly promising technique to address the demand of
data-hungry models. However, reliably assessing the quality of a 'synthesiser'
model's output is an open research question with significant associated risks
for high-stake domains. To address this challenge, we have designed a unique
confident data synthesis algorithm that introduces statistical confidence
guarantees through a novel extension of the Conformal Prediction framework. We
support our proposed algorithm with theoretical proofs and an extensive
empirical evaluation of five benchmark datasets. To show our approach's
versatility on ubiquitous real-world challenges, the datasets were carefully
selected for their variety of difficult characteristics: low sample count,
class imbalance and non-separability, and privacy-sensitive data. In all
trials, training sets extended with our confident synthesised data performed at
least as well as the original, and frequently significantly improved Deep
Learning performance by up to +65% F1-score.

通过引入一种基于置信度的数据合成算法，该算法通过对 Conformal Prediction 框架进行新的扩展，为模型输出提供统计置信度保证，并通过理论证明和对五个基准数据集的广泛实证评估来支持我们的算法。在面对低样本数、类别不均衡、不可分性和隐私敏感数据等多样困难特征的数据集挑战时，我们的算法在所有试验中都至少与原始数据表现相当，并在很多情况下可以将深度学习性能提升高达 65% 的 F1-score。