Saliency Guided

Saliency-guided methods enhance machine learning models by focusing training and inference on the most relevant input features, improving both performance and interpretability. Current research emphasizes integrating saliency into various architectures, including convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs), applying it to diverse tasks like image classification, object detection, and video analysis. This approach is significant because it addresses the "black box" nature of many deep learning models, fostering trust and enabling more effective model development and deployment across numerous applications.