Recent developments in media processing have been remarkable, accompanied by the advancement of machine learning. In the Internet of Things (IoT) society, information is frequently transmitted and received through various media, making compression of information crucial for reducing communication volume. Compression can be divided into two types: one, like MP3 or JPEG, reduces information as much as possible without significantly degrading the original information and reconstructs it at the time of use; the other omits part of the sensing to minimize information and reconstructs from that limited information, which is particularly referred to as compressive sensing.
Typical machine learning processes require substantial information. However, during the process of compressing information, important data may be lost, and models trained on such data often suffer from reduced inference accuracy. Therefore, we pose the question: Is it possible to use compressive sensing to appropriately compress only the information that is not very important for machine learning? We are engaged in research and development of compression technologies specialized for machine learning.
- Compressive Sensing Technology for Deep Learning
- Design of Learning Models Specialized for Compressed Sensing Images
