Papers › Learning Representations of Sets through Optimized Permutations

Learning Representations of Sets through Optimized Permutations

10 Dec 2018ICLR 2019 5arXiv:1812.03928archive 2025-07-28

Yan Zhang, Jonathon Hare, Adam Prügel-Bennett

Representations of sets are challenging to learn because operations on sets should be permutation-invariant. To this end, we propose a Permutation-Optimisation module that learns how to permute a set end-to-end. The permuted set can be further processed to learn a permutation-invariant representation of that set, avoiding a bottleneck in traditional set models. We demonstrate our model's ability to learn permutations and set representations with either explicit or implicit supervision on four datasets, on which we achieve state-of-the-art results: number sorting, image mosaics, classification from image mosaics, and visual question answering.

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