C
Christian Szegedy
Researcher at Google
Publications - 76
Citations - 197784
Christian Szegedy is an academic researcher from Google. The author has contributed to research in topics: Automated theorem proving & Deep learning. The author has an hindex of 31, co-authored 69 publications receiving 147148 citations. Previous affiliations of Christian Szegedy include Lawrence Berkeley National Laboratory & Cadence Design Systems.
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Explaining and Harnessing Adversarial Examples
TL;DR: The authors argue that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature, which is supported by new quantitative results while giving the first explanation of the most intriguing fact about adversarial examples: their generalization across architectures and training sets.
Proceedings Article
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
TL;DR: In this article, the authors show that training with residual connections accelerates the training of Inception networks significantly, and they also present several new streamlined architectures for both residual and non-residual Inception Networks.
Proceedings ArticleDOI
DeepPose: Human Pose Estimation via Deep Neural Networks
TL;DR: The pose estimation is formulated as a DNN-based regression problem towards body joints and a cascade of such DNN regres- sors which results in high precision pose estimates.
Posted Content
Going Deeper with Convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: A deep convolutional neural network architecture codenamed Inception is proposed that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Proceedings Article
Deep Neural Networks for Object Detection
TL;DR: This paper presents a simple and yet powerful formulation of object detection as a regression problem to object bounding box masks, and defines a multi-scale inference procedure which is able to produce high-resolution object detections at a low cost by a few network applications.