# Transfer Learning with Sparse Associative Memories

## TL;DR

This paper presents a new associative memory-based layer for neural networks that enables incremental learning and real-time training on embedded devices, with slight accuracy trade-offs demonstrated on ImageNet and other datasets.

## Contribution

Introduces a novel associative memory layer for neural networks supporting incremental learning and real-time training, enhancing flexibility and speed.

## Key findings

- Supports incremental learning in neural networks
- Enables real-time training on embedded devices
- Results show slight decrease in accuracy compared to traditional models

## 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.