The paper of our master thesis student Maxim Verwilst, titled “Deep image hashing based on twin-bottleneck hashing with variational autoencoders” is accepted for presentation at IEEE MMSP 2021!
Abstract: With the ever-increasing availability of data, the need for efficient and accurate image retrieval methods has become larger and larger. Deep hashing has proven to be a promising solution, by defining a hash function to convert the data into a manageable lower-dimensional representation. In this paper, we apply recent insights from the field of variational autoencoders to the field of deep image hashing, thus achieving an improvement over the current state of the art as shown by experimental evaluation. The code used in this paper is open-source and available on GitHub.
Figure: Schematic of the twin-bottleneck hashing architecture with proposed improvements.
