Dense Associative Memory for Image Classification with CNN (Resnet50)
This paper describes the integration of Dense Associative Memory (DAM) with Convolutional Neural Networks (CNNs) based Resnet50 architecture, to improve pattern recognition capabilities. Associative memory helps to store and retrieve many more patterns than the number of neurons in the network that helped contribute to enhanced performance in tasks such as image classification. The proposed concept aims to address the challenges of traditional memory systems in handling dense and complex patterns by using the feature extraction of Resnet50. We show the results of our experiments that are more reliable than the standard CNNs. In this problem, the network is presented with an image and the task is to label the image. It is difficult to solve the problem if the number of patterns is more or equal to the number of neurons in the network, or equivalently the number of pixels in an image. It can be solved by altering the standard energy function of associative memory, quadratic interactions between the neurons, by including in its higher-order interactions. We used the energy function that interacts with higher-order interactions so that one can store and accurately retrieve more patterns than the number of neurons. Our work underscores the potential of incorporating dense associative memory into deep learning architectures, paving the way for enhancements in pattern recognition concepts. This research contributes to the ongoing discourse on innovative strategies to improve the efficiency of neural networks for real-world applications. We performed our experiment on the Coffee Beans dataset that is available in Kaggle.