2020
AAAI
AAAI 2020
Exchangeable Generative Models with Flow Scans
Abstract
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.
🌉
Interdisciplinary Bridge
— Deep Learning and Machine Learning
🧭
Keyword Pioneer
— invertible flow
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Learning Types > Unsupervised Learning
Deep Learning > Architectures > Transformers
Deep Learning > Architectures > Neural Networks
Deep Learning > Models > Diffusion Models
Deep Learning > Models > Generative Models
Deep Learning > Learning Types > Self-Supervised Learning
Machine Learning > Learning Types > Generative Model