1 code implementation • ICCV 2021 • Terrance DeVries, Miguel Angel Bautista, Nitish Srivastava, Graham W. Taylor, Joshua M. Susskind
In this paper, we introduce Generative Scene Networks (GSN), which learns to decompose scenes into a collection of many local radiance fields that can be rendered from a free moving camera.
Ranked #1 on Scene Generation on VizDoom
no code implementations • 31 Mar 2021 • Eu Wern Teh, Terrance DeVries, Brendan Duke, Ruowei Jiang, Parham Aarabi, Graham W. Taylor
We further show that GIST and RIST can be combined with existing semi-supervised learning methods to boost performance.
1 code implementation • 21 Dec 2020 • Rylee Thompson, Elahe Ghalebi, Terrance DeVries, Graham W. Taylor
Generative models are now used to create a variety of high-quality digital artifacts.
2 code implementations • NeurIPS 2020 • Terrance DeVries, Michal Drozdzal, Graham W. Taylor
By refining the empirical data distribution before training, we redirect model capacity towards high-density regions, which ultimately improves sample fidelity, lowers model capacity requirements, and significantly reduces training time.
Ranked #2 on Conditional Image Generation on ImageNet 64x64
1 code implementation • ECCV 2020 • Eu Wern Teh, Terrance DeVries, Graham W. Taylor
Additionally, our proposed fast moving proxies also addresses small gradient issue of proxies, and this component synergizes well with low temperature scaling and Global Max Pooling.
Ranked #2 on Image Retrieval on CARS196
1 code implementation • 11 Jul 2019 • Terrance DeVries, Adriana Romero, Luis Pineda, Graham W. Taylor, Michal Drozdzal
We show that FJD can be used as a promising single metric for cGAN benchmarking and model selection.
no code implementations • 6 Jun 2019 • Terrance DeVries, Ishan Misra, Changhan Wang, Laurens van der Maaten
The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset.
no code implementations • 2 Jul 2018 • Terrance DeVries, Graham W. Taylor
The first is producing spatial uncertainty maps, from which a clinician can observe where and why a system thinks it is failing.
4 code implementations • 13 Feb 2018 • Terrance DeVries, Graham W. Taylor
Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong.
27 code implementations • 15 Aug 2017 • Terrance DeVries, Graham W. Taylor
Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks.
Ranked #1 on Out-of-Distribution Generalization on ImageNet-W
no code implementations • 9 Mar 2017 • Dhanesh Ramachandram, Terrance DeVries
We present a method for skin lesion segmentation for the ISIC 2017 Skin Lesion Segmentation Challenge.
no code implementations • 4 Mar 2017 • Terrance DeVries, Dhanesh Ramachandram
We present a deep learning approach to the ISIC 2017 Skin Lesion Classification Challenge using a multi-scale convolutional neural network.
3 code implementations • 17 Feb 2017 • Terrance DeVries, Graham W. Taylor
Our main insight is to perform the transformation not in input space, but in a learned feature space.