# Exchangeable Generative Models with Flow Scans

## TL;DR

FlowScan introduces a novel exchangeable density estimation method combining invertible flows with sorted scans, capturing intra-set dependencies and achieving state-of-the-art results on point cloud and image set modeling.

## Contribution

FlowScan is the first method to apply sequential density estimation to exchangeable data without permutation averaging, leveraging intra-set dependencies.

## Key findings

- Achieves state-of-the-art performance on point cloud modeling.
- Effectively models intra-set dependencies in exchangeable data.
- Outperforms previous methods in image set modeling.

## Abstract

In this work, we develop a new approach to generative density estimation for exchangeable, non-i.i.d. data. The proposed framework, FlowScan, combines invertible flow transformations with a sorted scan to flexibly model the data while preserving exchangeability. Unlike most existing methods, FlowScan exploits the intradependencies within sets to learn both global and local structure. FlowScan represents the first approach that is able to apply sequential methods to exchangeable density estimation without resorting to averaging over all possible permutations. We achieve new state-of-the-art performance on point cloud and image set modeling.