Optimizing 1-Nearest Prototype Classifiers
By Paul Wohlhart, Martin Köstinger, Michael Donoser, Peter M. Roth, Horst Bischof. 2013
20 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
- Luc Van Gool
- Michael Tschannen
- Lukas Cavigelli
- Luca Benini
- Radu Timofte
- Rishabh Misra
- Mengting Wan
- Eirikur Agustsson
In Computer Vision and Pattern Recognition
- Radu Timofte
- Luc Van Gool
- Eirikur Agustsson
- Michael Tschannen
- Fabian Mentzer
- Chen Change Loy
- Ming-Hsuan Yang
- Dacheng Tao
Citations in the corpus
- Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations (2017, background): “Arguably most related to our approach is [41], which also employs continuation for nearest neighbor assignments, but in the context of learning a supervised prototype classifier.”
- Soft-to-Hard Vector Quantization for End-to-End Learned Compression of Images and Neural Networks (2017, methodology): “Arguably most related to our approach is (Wohlhart et al., 2013), which also employs continuation for nearest neighbor assignments, but in the context of learning a supervised prototype classifier.”
- Decomposing fit semantics for product size recommendation in metric spaces (2018, background): “Another study proposes a novel algorithm for deriving optimal prototypes, specifically for the 1-NN setting [11].”