22k citations · 25.4k across the 24 of their papers we have counts for
5 papers · 1 filter
Training Deep Neural Networks on Noisy Labels with Bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov +3
Current state-of-the-art deep learning systems for visual object recognition and detection use purely supervised training with regularization such as dropout to avoid overfitting.…
Self-informed neural network structure learning
David Warde-Farley, Andrew Rabinovich, Dragomir Anguelov
We study the problem of large scale, multi-label visual recognition with a large number of possible classes. We propose a method for augmenting a trained neural network classifier…
Scalable, High-Quality Object Detection
Christian Szegedy, Scott Reed, Dumitru Erhan +2
Current high-quality object detection approaches use the scheme of salience-based object proposal methods followed by post-classification using deep convolutional features. This sp…
Going Deeper with Convolutions
Christian Szegedy, Wei Liu, Yangqing Jia +6
We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in th…
Self-taught Object Localization with Deep Networks
Loris Bazzani, Alessandro Bergamo, Dragomir Anguelov +1
This paper introduces self-taught object localization, a novel approach that leverages deep convolutional networks trained for whole-image recognition to localize objects in images…