Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

TL;DR

The paper introduces the Set Transformer, an attention-based neural network designed for permutation-invariant set modeling, achieving state-of-the-art results with reduced computational complexity.

Contribution

It presents a novel attention-based architecture for set-structured data, incorporating an efficient attention scheme inspired by sparse Gaussian processes.

Findings

Achieves state-of-the-art performance on various set-based tasks.

Reduces attention computation from quadratic to linear complexity.

Demonstrates theoretical advantages of the model.

Abstract

Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances. Since solutions to such problems do not depend on the order of elements of the set, models used to address them should be permutation invariant. We present an attention-based neural network module, the Set Transformer, specifically designed to model interactions among elements in the input set. The model consists of an encoder and a decoder, both of which rely on attention mechanisms. In an effort to reduce computational complexity, we introduce an attention scheme inspired by inducing point methods from sparse Gaussian process literature. It reduces the computation time of self-attention from quadratic to linear in the number of elements in the set. We show that our model is theoretically attractive and we evaluate it on a range of tasks, demonstrating the state-of-the-art performance compared to recent methods for set-structured data.