Reviews: Deep Set Prediction Networks

Summary: This paper presents an approach for solving machine learning tasks that require the prediction to be presented in the form of a set. The authors propose to use the set encoder (which is composed of permutation-invariant operations) at the prediction phase by finding an output set with an optimization procedure. As the model output is a vector of continuous features for each set element, it can be done by means of nested gradient descent optimization. In order to solve the task of set prediction for external feature vector, the work suggests a combined loss function that encourages the representation of ground truth to be close to obtained features. Results are shown on MNIST and CLEVR datasets and outperform those of an MLP baseline.