Lightweight Lossy Compression of Biometric Patterns via Denoising Autoencoders

By Davide Del Testa, Michele Rossi. 2015

72 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine

How researchers see this paper

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

In Biomedical Engineering

1. Francisco Herrera
2. Andrew Cunningham
3. Lucas Theis
4. Ferenc Huszár
5. Wenzhe Shi

In Cardiology and Cardiovascular Medicine

1. Andrew Cunningham
2. Lucas Theis
3. Ferenc Huszár
4. Wenzhe Shi

Citations in the corpus

• A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, background): “• In [123] the authors suggest the use of AEs to compress biometric data, such as blood pressure or heart rate, retrieved by wearable devices.”
• Lossy Image Compression with Compressive Autoencoders (2017, methodology): “Existing transformations have typically been manually chosen (e.g., the DCT transformation used in JPEG) or have been optimized for a task different from lossy compression (e.g. Testa & Rossi, 2016, used denoising autoencoders for compression).”