18 citations · 18 across the 1 of their papers we have counts for
3 papers · 1 filter
Learn to Predict Sets Using Feed-Forward Neural Networks
Hamid Rezatofighi, Tianyu Zhu, Roman Kaskman +6
This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a se…
Unsupervised Task Design to Meta-Train Medical Image Classifiers
Gabriel Maicas, Cuong Nguyen, Farbod Motlagh +2
Meta-training has been empirically demonstrated to be the most effective pre-training method for few-shot learning of medical image classifiers (i.e., classifiers modeled with smal…
Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks
S. Hamid Rezatofighi, Roman Kaskman, Farbod T. Motlagh +4
Many real-world problems, e.g. object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which…