22k citations · 28.7k across the 25 of their papers we have counts for
4 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.…
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…
Evaluation of Output Embeddings for Fine-Grained Image Classification
Zeynep Akata, Scott Reed, Daniel Walter +2
Image classification has advanced significantly in recent years with the availability of large-scale image sets. However, fine-grained classification remains a major challenge due…