In particular, we explore the effect of replacing the first layers of various deep architectures with Gabor layers (i.e. convolutional layers with filters that are based on learnable Gabor parameters) on robustness against adversarial attacks. We observe that architectures with Gabor layers gain a ...
Gabor filters are creatively incorporated into Conv2D layers to extract significant features, enhance accurate classification, and mitigate computational complexity. 3. An attention-based dual channel Gabor network (ADCGNet) with five key dimensions to enhance feature maps for ECG rhythm detection in sca...
A network structure similar to U-net is adopted as a generator, and skip connections are added between i and n−i at each layer to simulate U-net, where n is the total number of layers of the network. Not only can the path be shrunk for context information, but the symmetric ...
In addition, global self-attention mechanisms and Transformer layers are also incorporated into the U-Net framework to capture global contexts. Through extensive testing on two benchmark datasets, we show that the Gabor filter-embedded U-Net with Transformer encoders can enhance the robustness of ...
Gabor Layers Enhance Network Robustness. In Proceedings of the ECCV 2020, Glasgow, UK, 23–28 August 2020; Volume 12354, pp. 450–466. [Google Scholar] Luan, S.; Chen, C.; Zhang, B.; Han, J.; Liu, J. Gabor Convolutional Networks. IEEE Trans. Image Process. 2018, 27, 4357–4366...
The convolutional neural network (CNN) is a type of multi-layer neural network, which extracts features by combining convolution, pooling, and activation layers. The CNN is widely used in the field of pattern recognition. Many researchers have applied the CNN to traffic sign recognition and detect...
It's the best choice when the two circles are just the adjacent layers around the center point. If the radii are both too small, the two circles are too near and the information is not representative, especially after subtraction. In contrast, if the local region is too sparse, the ...