Jeffrey Gu

Jeffrey Gu

PhD candidate at Stanford University

Talks and presentations

Unsupervised Hyperbolic Representations Learning for Computer Vision

October 24, 2022

Tutorial, Hyperbolic Representation Learning for Computer Vision Tutorial, European Conference on Computer Vision (ECCV) 2022, Tel-Aviv, Israel

As part of our ECCV 2022 tutorial Hyperbolic Representation Learning for Computer Vision with Pascal Mettes, Mina Ghadimi Atigh, Martin Keller-Ressel, and Serena Yeung, I gave the third talk on recent research in unsupervised hyperbolic representation learning methods in computer vision. Slides for the talks can be found here and notebooks walking through the basics of hyperbolic representation learning and some recent research can be found here.

Learning Hyperbolic Representations for Unsupervised 3D Segmentation

December 11, 2020

Contributed Talk, NeurIPS Differential Geometry meets Deep Learning (DiffGeo4DL) Workshop 2020, Virtual

Abstract: There exists a need for unsupervised 3D segmentation on complex volumetric data, particularly when annotation ability is limited or discovery of new categories is desired. Using the observation that much of 3D volumetric data is innately hierarchical, we propose learning effective representations of 3D patches for unsupervised segmentation through a variational autoencoder (VAE) with a hyperbolic latent space and a proposed gyroplane convolutional layer, which better models the underlying hierarchical structure within a 3D image. We also introduce a hierarchical triplet loss and multi-scale patch sampling scheme to embed relationships across varying levels of granularity. We demonstrate the effectiveness of our hyperbolic representations for unsupervised 3D segmentation on a hierarchical toy dataset, BraTS whole tumor dataset, and cryogenic electron microscopy data. With Joy Hsu.