NIPS Proceedingsβ

Deep Set Prediction Networks

Part of: Advances in Neural Information Processing Systems 32 (NIPS 2019) pre-proceedings

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Conference Event Type: Poster


Current approaches for predicting sets from feature vectors ignore the unordered nature of sets 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 the set of bounding boxes of objects in an image, and predict the set of attributes of these objects.