Entropic associative memory for real world images
This work extends a computational memory model to more realistic data, but it is incremental as it applies an existing method to new types of images.
The authors tackled the problem of testing the entropic associative memory model on complex real-world images, showing that it successfully stores, recognizes, and retrieves images of animals and vehicles while generating meaningful association chains.
The entropic associative memory (EAM) is a computational model of natural memory incorporating some of its putative properties of being associative, distributed, declarative, abstractive and constructive. Previous experiments satisfactorily tested the model on structured, homogeneous and conventional data: images of manuscripts digits and letters, images of clothing, and phone representations. In this work we show that EAM appropriately stores, recognizes and retrieves complex and unconventional images of animals and vehicles. Additionally, the memory system generates meaningful retrieval association chains for such complex images. The retrieved objects can be seen as proper memories, associated recollections or products of imagination.