Deep Learning with Yacine on MSNOpinion

What are 1x1 convolutions in deep learning – explained simply

Understand how 1x1 convolutions work and why they’re essential in modern neural network architectures like ResNet and ...
The New York Times

CNN, Unwanted by Netflix, Is Excluded From a Sale, for Now

The prospect of Paramount’s buying Warner Bros. Discovery had led CNN journalists to wonder if the channel may be combined with CBS News. Instead, CNN will remain in a separate corporate entity. By ...
Scientific Research Publishing

Lau, K., Po, L. and Rehman, Y. (2023) Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNN.

ABSTRACT: Visual Attention Networks (VANs) leveraging Large Kernel Attention (LKA) have demonstrated remarkable performance in diverse computer vision tasks, often outperforming Vision Transformers ...
Scientific Research Publishing

Dual-Dilated Large Kernel Convolution for Visual Attention Network ()

Visual Attention Networks (VANs) leveraging Large Kernel Attention (LKA) have demonstrated remarkable performance in diverse computer vision tasks, often outperforming Vision Transformers (ViTs) in ...
IEEE

TCINet: Transformer-CNN Interaction Network for Space Non-Cooperative Object Saliency Detection

Abstract: Space Non-Cooperative Object Detection (SNCOD) is an essential part of Space Situation Awareness (SSA). The localization and segmentation capabilities of the Salient Object Detection (SOD) ...
IEEE

Calibration of Compact Resist Model Through CNN Training

Abstract: We notice that the compact resist model can be mapped to a simple CNN (convolutional neural network): convolutional layer corresponds to convolutions between input images and resist kernels, ...
IEEE

Stream-ViT: Learning Streamlined Convolutions in Vision Transformer

Abstract: Recently Vision Transformer (ViT) and Convolution Neural Network (CNN) start to emerge as a hybrid deep architecture with better model capacity, generalization, and latency trade-off. Most ...
IEEE

TCCU-Net: Transformer and CNN Collaborative Unmixing Network for Hyperspectral Image

Abstract: In recent years, deep-learning-based hyperspectral unmixing techniques have garnered increasing attention and made significant advancements. However, relying solely on the use of ...