Short receptive field

Information

• A receptive field is a term used in machine learning to describe the size of the area in an image or sequence that the model is considering at one time.
• A short receptive field is a small area that a model considers at once.
• Short receptive fields are used in neural networks and deep learning models to enable the model to capture local patterns in the input data.
• When a model has a small receptive field, it can identify small, intricate details in the input data.
• Using short receptive fields can improve the accuracy of image classification and object detection in machine learning models.
• Short receptive fields can be combined with larger receptive fields to create multi-scale features, which allow the model to capture both small and large details in the input data.
• In some cases, using too small of a receptive field can lead to overfitting or loss of important contextual information in the input data, so it's important to balance the size of the receptive field with the complexity of the model and the nature of the input data.