1.9k citations · 3.7k across the 35 of their papers we have counts for
9 papers · 1 filter
Learning by Asking Questions
Ishan Misra, Ross Girshick, Rob Fergus +3
We introduce an interactive learning framework for the development and testing of intelligent visual systems, called learning-by-asking (LBA). We explore LBA in context of the Visu…
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Benjamin Graham, Martin Engelcke, Laurens van der Maaten
Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos…
Separating Self-Expression and Visual Content in Hashtag Supervision
Andreas Veit, Maximilian Nickel, Serge Belongie +1
The variety, abundance, and structured nature of hashtags make them an interesting data source for training vision models. For instance, hashtags have the potential to significantl…
CondenseNet: An Efficient DenseNet using Learned Group Convolutions
Gao Huang, Shichen Liu, Laurens van der Maaten +1
Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unp…
Countering Adversarial Images using Input Transformations
Chuan Guo, Mayank Rana, Moustapha Cisse +1
This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system. Spe…
Memory-Efficient Implementation of DenseNets
Geoff Pleiss, Danlu Chen, Gao Huang +3
The DenseNet architecture is highly computationally efficient as a result of feature reuse. However, a naive DenseNet implementation can require a significant amount of GPU memory:…
Submanifold Sparse Convolutional Networks
Benjamin Graham, Laurens van der Maaten
Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc. Whilst some of this data is naturally dense (for instance…
Inferring and Executing Programs for Visual Reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten +4
Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes. As a r…
Multi-Scale Dense Networks for Resource Efficient Image Classification
Gao Huang, Danlu Chen, Tianhong Li +3
In this paper we investigate image classification with computational resource limits at test time. Two such settings are: 1. anytime classification, where the network's prediction…