1 day ago · This highlights that not all neural network architectures are equally easy to optimize. ResNet uses a technique called “residual mapping” to combat this issue.

5 days ago · We address this limitation by introducing Residual KAN, which incorporates the Kolmogorov-Arnold Network (KAN) within the CNN framework as a residual component.

20 hours ago · Residual Blocks: The architecture employs residual blocks that allow gradients to flow more easily, making it feasible to train networks with hundreds of layers ...

5 days ago · As fundamental building blocks in computer vision, Convolutional Neural Networks (CNNs) have demonstrated excellent performance in image classification, object ...

2 days ago · The most commonly used feature extraction network in convolutional neural networks is ResNet [39] . We use this convolutional neural network to learn the ...

13 hours ago · Residual Learning: refers to the idea that instead of learning the desired output directly, ResNet learns the residuals, or the difference between the input and ...

3 hours ago · This study proposes a novel Dense 3D Convolutional Residual Network (D3DCNN_ResNet) to recognize students' expressions and behaviors in English classrooms.

7 hours ago · In 2015, two techniques were developed concurrently to train very deep networks: highway network and residual neural network (ResNet). The ...

7 hours ago · Residual Learning: The use of skip connections promotes feature reuse and mitigates the vanishing gradient problem, enabling the training of deeper networks.

4 days ago · In this paper, a Channel-Attention Residual Network (CARNet) is proposed to significantly reduce the complexity of ResNet models. By designing a lightweight ...