Going deeper with convolutions
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Sca...
Publications
4 shownDeep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well ...
Rethinking the Inception Architecture for Computer Vision
Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to bec...
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has b...
Frequent Co-Authors
Researcher Info
- h-index
- 4
- Publications
- 4
- Citations
- 89,721
- Institution
- Google (United States)
External Links
Identifiers
- ORCID
- 0000-0003-0544-2791
Impact Metrics
h-index
4
h-index: Number of publications with at least h citations each.