Implicit Attention

Implicit attention in deep learning focuses on indirectly learning relevant information by suppressing irrelevant features, rather than explicitly highlighting important regions. Current research explores this concept within various architectures, including convolutional neural networks (CNNs) enhanced with attention modules like Squeeze-and-Excitation and CBAM, and implicit neural representations (INRs) used for tasks such as image super-resolution and deepfake detection. This approach offers advantages in efficiency and generalizability compared to explicit attention methods, particularly for high-resolution or complex data, leading to improved performance in image processing, medical imaging, and other computer vision applications.