We study the problem of predicting a set from a feature vector with a deep neural network. Existing approaches ignore the set structure of the problem and suffer from discontinuity issues as a result. We propose a general model for predicting sets that properly respects the structure of sets and avoids this problem. With a single feature vector as input, we show that our model is able to auto-encode point sets, predict bounding boxes of the set of objects in an image, and predict the attributes of these objects in an image.
Deep Set Prediction Networks REF introduced an interesting approach to predict sets from a feature vector which is in contrast to predicting an output using sets.
Deep Set Prediction Networks
Advances in Neural Information Processing Systems 32 (NeurIPS 2019)
Mathematics Computer Science