Multiset-equivariant set prediction with approximate implicit differentiation
conference paper
Most set prediction models in deep learning use set-equivariant operations, but they actually operate on multisets. We show that set-equivariant functions cannot represent certain functions on multisets, so we introduce the more appropriate notion of multiset-equivariance. We identify that the existing Deep Set Prediction Network (DSPN) can be multiset-equivariant without being hindered by set-equivariance and improve it with approximate implicit differentiation, allowing for better optimization while being faster and saving memory. In a range of toy experiments, we show that the perspective of multiset-equivariance is beneficial and that our changes to DSPN achieve better results in most cases. On CLEVR object property prediction, we substantially improve over the state-of-the-art Slot Attention from 8% to 77% in one of the strictest evaluation metrics because of the benefits made possible by implicit differentiation. (C) 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.
Topics
TNO Identifier
985095
Publisher
International Conference on Learning Representations, ICLR
Source title
ICLR 2022 - 10th International Conference on Learning Representations
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