论文标题
有条件的固定生成变压器
Conditional Set Generation with Transformers
论文作者
论文摘要
一组是独特元素的无序集合，但是许多生成集合的机器学习模型会施加隐式或明确的订购。由于模型性能可以取决于顺序的选择，因此任何特定的订单都可以导致次优结果。另一种解决方案是使用置换量表集合发电机，该生成器未指定订单。这种生成器的一个示例是DeepSet预测网络（DSPN）。我们介绍了Transformer SET预测网络（TSPN），这是一种基于变压器的灵活置换等值模型，该模型基于变压器，该模型在预测的集合元素的质量和预测大小的准确性上构建了DSPN和优于DSPN。我们在MNIST-AS-AS-POINT-CLOUDS（SET-MNIST）上测试了我们的点云生成和CLEVR的模型以进行对象检测。
A set is an unordered collection of unique elements--and yet many machine learning models that generate sets impose an implicit or explicit ordering. Since model performance can depend on the choice of order, any particular ordering can lead to sub-optimal results. An alternative solution is to use a permutation-equivariant set generator, which does not specify an order-ing. An example of such a generator is the DeepSet Prediction Network (DSPN). We introduce the Transformer Set Prediction Network (TSPN), a flexible permutation-equivariant model for set prediction based on the transformer, that builds upon and outperforms DSPN in the quality of predicted set elements and in the accuracy of their predicted sizes. We test our model on MNIST-as-point-clouds (SET-MNIST) for point-cloud generation and on CLEVR for object detection.
