We introduce GEDI, a Bayesian framework that combines existing
self-supervised learning objectives with likelihood-based generative models.
This framework leverages the benefits of both GEnerative and DIscriminative
approaches, resulting in improved symbolic representations over standalone
solutions. Additionally, GEDI can be easily integrated and trained jointly with
existing neuro-symbolic frameworks without the need for additional supervision
or costly pre-training steps. We demonstrate through experiments on real-world
data, including SVHN, CIFAR10, and CIFAR100, that GEDI outperforms existing
self-supervised learning strategies in terms of clustering performance by a
significant margin. The symbolic component further allows it to leverage
knowledge in the form of logical constraints to improve performance in the
small data regime.

介绍了 GEDI，一种结合了现有自监督学习目标和基于似然的生成模型的贝叶斯框架，它结合了生成性和判别性方法的优点，可以在不需要额外监督或昂贵的预训练步骤的情况下轻松集成和联合训练。 在真实世界的数据上进行的实验表明，GEDI 在聚类性能方面优于现有的自监督学习策略，并且符号组件进一步允许其利用逻辑约束形式的知识来改善小样本数据情况下的性能。