Researchers from Yale University and the Gatsby Unit & SWC, UCL have presented an understanding of the mechanisms behind localized receptive field emergence. Building upon previous work, researchers ...

1 Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou, China 2 First Institute of Oceanography and Key Laboratory of Marine Science and ...

and the limited capacity of Convolutional Neural Networks (CNNs) to capture global contextual information. ViTs suffer from quadratic computational complexity while excelling in fitting capabilities ...

Convolutional Neural Networks (CNN) are a class of machine learning ... V4 and IT, progressive increase of receptive field size and complexity. These features, combined with the evidence that ...

What are convolutional neural networks in ... for deep learning and machine learning algorithms in the field of artificial neural networks, including segmentation, object detection and image ...

Abstract: The convolutional neural network (CNN) has a poor performance in nonuniform and edge regions due to the limitation of fixed receptive field. At the same time, feature stacking of input data ...

The interplay between data symmetries and network architecture is key for efficient learning in neural networks. Convolutional neural networks perform well in image ... for this phenomenon in a simple ...

Abstract: Deep semantic segmentation networks perform better in cloud detection of satellite imagery than traditional methods due to their ability to extract high-level features over a large receptive ...