Entropic associative memory for real world images

• URL: http://arxiv.org/abs/2405.12500v1
• Date: Tue, 21 May 2024 05:00:30 GMT
• Title: Entropic associative memory for real world images
• Authors: Noé Hernández, Rafael Morales, Luis A. Pineda,
• Abstract summary: We show that EAM appropriately stores, recognizes and retrieves complex and unconventional images of animals and vehicles. The retrieved objects can be seen as proper memories, associated recollections or products of imagination.
• Score: 0.7373617024876725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Modeling Visual Memorability Assessment with Autoencoders Reveals Characteristics of Memorable Images [2.4861619769660637]
  Image memorability refers to the phenomenon where certain images are more likely to be remembered than others. We modeled the subjective experience of visual memorability using an autoencoder based on VGG16 Convolutional Neural Networks (CNNs) We investigated the relationship between memorability and reconstruction error, assessed latent space representations distinctiveness, and developed a Gated Recurrent Unit (GRU) model to predict memorability likelihood.
  arXiv  Detail & Related papers  (2024-10-19T22:58:33Z)
• Psychometry: An Omnifit Model for Image Reconstruction from Human Brain Activity [60.983327742457995]
  Reconstructing the viewed images from human brain activity bridges human and computer vision through the Brain-Computer Interface. We devise Psychometry, an omnifit model for reconstructing images from functional Magnetic Resonance Imaging (fMRI) obtained from different subjects.
  arXiv  Detail & Related papers  (2024-03-29T07:16:34Z)
• Associative Memories in the Feature Space [68.1903319310263]
  We propose a class of memory models that only stores low-dimensional semantic embeddings, and uses them to retrieve similar, but not identical, memories. We demonstrate a proof of concept of this method on a simple task on the MNIST dataset.
  arXiv  Detail & Related papers  (2024-02-16T16:37:48Z)
• Improving Image Recognition by Retrieving from Web-Scale Image-Text Data [68.63453336523318]
  We introduce an attention-based memory module, which learns the importance of each retrieved example from the memory. Compared to existing approaches, our method removes the influence of the irrelevant retrieved examples, and retains those that are beneficial to the input query. We show that it achieves state-of-the-art accuracies in ImageNet-LT, Places-LT and Webvision datasets.
  arXiv  Detail & Related papers  (2023-04-11T12:12:05Z)
• Entropic Associative Memory for Manuscript Symbols [0.0]
  Manuscript symbols can be stored, recognized and retrieved from an entropic digital memory that is associative and distributed but yet declarative. We discuss the operational characteristics of the entropic associative memory for retrieving objects with both complete and incomplete information.
  arXiv  Detail & Related papers  (2022-02-17T02:29:33Z)
• Associative Memories via Predictive Coding [37.59398215921529]
  Associative memories in the brain receive and store patterns of activity registered by the sensory neurons. We present a novel neural model for realizing associative memories based on a hierarchical generative network that receives external stimuli via sensory neurons.
  arXiv  Detail & Related papers  (2021-09-16T15:46:26Z)
• Remember What You have drawn: Semantic Image Manipulation with Memory [84.74585786082388]
  We propose a memory-based Image Manipulation Network (MIM-Net) to generate realistic and text-conformed manipulated images. To learn a robust memory, we propose a novel randomized memory training loss. Experiments on the four popular datasets show the better performance of our method compared to the existing ones.
  arXiv  Detail & Related papers  (2021-07-27T03:41:59Z)
• Constellation: Learning relational abstractions over objects for compositional imagination [64.99658940906917]
  We introduce Constellation, a network that learns relational abstractions of static visual scenes. This work is a first step in the explicit representation of visual relationships and using them for complex cognitive procedures.
  arXiv  Detail & Related papers  (2021-07-23T11:59:40Z)
• An Entropic Associative Memory [0.0]
  We use intrinsic-Indeterminate Computing to retrieve associative memory registers that hold representations of individual objects. The system has been used to model a visual memory holding the representations of hand-written digits. The similarity between the cue and the object recovered in memory operations depends on the entropy of the memory register.
  arXiv  Detail & Related papers  (2020-09-28T04:24:21Z)
• Self-Attentive Associative Memory [69.40038844695917]
  We propose to separate the storage of individual experiences (item memory) and their occurring relationships (relational memory) We achieve competitive results with our proposed two-memory model in a diversity of machine learning tasks.
  arXiv  Detail & Related papers  (2020-02-10T03:27:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.