# Transfer Learning with Sparse Associative Memories

Source: [Transfer Learning with Sparse Associative Memories](<https://www.sota2.com/research/paper/68fa66651e209131cea10168>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:40.524Z

Authors: Quentin Jodelet, Vincent Gripon, Masafumi Hagiwara

Published: 2019-04-04T09:16:30.000Z

## Abstract

In this paper, we introduce a novel layer designed to be used as the output of pre-trained neural networks in the context of classification. Based on Associative Memories, this layer can help design Deep Neural Networks which support incremental learning and that can be (partially) trained in real time on embedded devices. Experiments on the ImageNet dataset and other different domain specific datasets show that it is possible to design more flexible and faster-to-train Neural Networks at the cost of a slight decrease in accuracy.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1904.02420>)

[PDF](<https://arxiv.org/pdf/1904.02420.pdf>)