Search Results for author:
Found 13 papers, 8 papers with code
Unconstrained Scene Generation with Locally Conditioned Radiance Fields
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
The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation
We further show that GIST and RIST can be combined with existing semi-supervised learning methods to boost performance.
Building LEGO Using Deep Generative Models of Graphs
Generative models are now used to create a variety of high-quality digital artifacts.
Instance Selection for GANs
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
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
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
On the Evaluation of Conditional GANs
We show that FJD can be used as a promising single metric for cGAN benchmarking and model selection.
Does Object Recognition Work for Everyone?
The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset.
Leveraging Uncertainty Estimates for Predicting Segmentation Quality
The first is producing spatial uncertainty maps, from which a clinician can observe where and why a system thinks it is failing.
Learning Confidence for Out-of-Distribution Detection in Neural Networks
Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong.
Improved Regularization of Convolutional Neural Networks with Cutout
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
LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network
We present a method for skin lesion segmentation for the ISIC 2017 Skin Lesion Segmentation Challenge.
Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks
We present a deep learning approach to the ISIC 2017 Skin Lesion Classification Challenge using a multi-scale convolutional neural network.
Dataset Augmentation in Feature Space
Our main insight is to perform the transformation not in input space, but in a learned feature space.
