Terrance DeVries
Terrance DeVries
PhD Candidate, University of Guelph
Verified email at uoguelph.ca - Homepage
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Improved regularization of convolutional neural networks with cutout
T DeVries, GW Taylor
arXiv preprint arXiv:1708.04552, 2017
Dataset augmentation in feature space
T DeVries, GW Taylor
International Conference on Learning Representations (ICLR) Workshop, 2017
Learning confidence for out-of-distribution detection in neural networks
T DeVries, GW Taylor
arXiv preprint arXiv:1802.04865, 2018
Multi-task learning of facial landmarks and expression
T DeVries, K Biswaranjan, GW Taylor
2014 Canadian Conference on Computer and Robot Vision, 98-103, 2014
Does object recognition work for everyone?
T DeVries, I Misra, C Wang, L van der Maaten
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
Leveraging uncertainty estimates for predicting segmentation quality
T DeVries, GW Taylor
arXiv preprint arXiv:1807.00502, 2018
Skin lesion classification using deep multi-scale convolutional neural networks
T DeVries, D Ramachandram
arXiv preprint arXiv:1703.01402, 2017
On the evaluation of conditional gans
T DeVries, A Romero, L Pineda, GW Taylor, M Drozdzal
arXiv preprint arXiv:1907.08175, 2019
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
EW Teh, T DeVries, GW Taylor
European Conference on Computer Vision, 2020
LesionSeg: semantic segmentation of skin lesions using deep convolutional neural network
D Ramachandram, T DeVries
arXiv preprint arXiv:1703.03372, 2017
Instance selection for GANs
T DeVries, M Drozdzal, GW Taylor
Advances in Neural Information Processing Systems, 2020
Building LEGO Using Deep Generative Models of Graphs
R Thompson, E Ghalebi, T DeVries, GW Taylor
NeurIPS Workshop on Machine Learning for Engineering Modeling, Simulation …, 2020
Unconstrained Scene Generation with Locally Conditioned Radiance Fields
T DeVries, MA Bautista, N Srivastava, GW Taylor, JM Susskind
arXiv preprint arXiv:2104.00670, 2021
The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation
E Wern Teh, T DeVries, B Duke, R Jiang, P Aarabi, GW Taylor
arXiv e-prints, arXiv: 2103.17105, 2021
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