Discriminative Autoencoders for Small Targets Detection
By Sébastien Razakarivony, Frédéric Jurie. 2014
30 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Francisco Herrera
- Salvador Garcia
- M. J. Jesús
- Francisco Charte
- Dilip Ravi Kiran
- Mathew Thomas
- David Charte
- Ranjith Parakkal
In Computer Vision and Pattern Recognition
- Salvador Garcia
- Francisco Herrera
- M. J. Jesús
- Dilip Ravi Kiran
- Mathew Thomas
- Francisco Charte
- David Charte
- Ranjith Parakkal
Citations in the corpus
- A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, background): “Introduced in [89], the discriminative AE uses the class information of instances in order to build a manifold where positive samples are gathered and negative samples are pushed away.”
- An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos (2018, background): “In similar work by [80], discriminative autoencoders aim at learning low-dimensional discriminative representations for positive ( X + ) and negative ( X − ) classes of data.”