| dc.description.abstract | In recent years, with the rapid development of artificial intelligence (AI), it has significantly transformed our lives and various domains, and its impact is difficult to quantify. AI has even surpassed humans in performance in certain areas such as Go, chess, and Texas Hold’em poker. However, the decision- making process of artificial intelligence (AI) is often considered a black box, raising the question of how it actually makes decisions.
This research proposes a deep learning model based on convolutional neural networks (CNNs) that incorporates the concepts of multi-layer SOM and the functioning of the visual cortex in the human brain to provide interpretability to the decision-making process of deep learning models. This model uses a multi-layer architecture for image classification. When an image is inputted, it undergoes Gaussian convolution and feature enhancement mechanisms. The image features are then combined in a temporal sequence and propagated to the next layer, mimicking the operation of the visual cortex in processing visual signals. Lower-level neurons integrate fine-grained information and transmit it hierarchically through the network structure. Finally, a fully connected layer is used to convert the output into the classification result of the image.
In our experiment, two datasets, namely MNIST and Fashion-MNIST, were used, both yielding favorable performance. At each stage, the features were explained, and through feature visualization, it was observed that each layer had its unique significance. This is of paramount importance for explainable AI, providing new insights and methods for the development of machine learning and related fields. | en_US |