Minimalistic CNN-based ensemble model for gender prediction from face images

By Grigory Antipov, Sid-Ahmed Berrani, Jean-Luc Dugelay. 2016

130 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Stephen J. Roberts
2. Thomas Lansdall-Welfare
3. Nello Cristianini
4. Sen Jia
5. Sid Ghoshal
6. Francis R. Bach
7. Alessandro Moschitti
8. Trevor Darrell

In Signal Processing

1. Thomas Lansdall-Welfare
2. Stephen J. Roberts
3. Nello Cristianini
4. Sen Jia
5. Sid Ghoshal

Citations in the corpus

• Gender Classification by Deep Learning on Millions of Weakly Labelled Images (2016, methodology): “deeper networks plus weakly labelled data can improve on previously reported CNN approaches [4]).”
• Thresholded ConvNet ensembles: neural networks for technical forecasting (2018, methodology): “An e ective technique in image processing involves homogeneous ensembling of multiple copies of the same CNN architecture, averaging across the class assignments of the constituent models (Krizhevsky et al, 2012; Antipova et al, 2016).”