  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.
