DeepAI AI Chat
Log In Sign Up

Gabor Convolutional Networks

by   Shangzhen Luan, et al.
Beihang University

Steerable properties dominate the design of traditional filters, e.g., Gabor filters, and endow features the capability of dealing with spatial transformations. However, such excellent properties have not been well explored in the popular deep convolutional neural networks (DCNNs). In this paper, we propose a new deep model, termed Gabor Convolutional Networks (GCNs or Gabor CNNs), which incorporates Gabor filters into DCNNs to enhance the resistance of deep learned features to the orientation and scale changes. By only manipulating the basic element of DCNNs based on Gabor filters, i.e., the convolution operator, GCNs can be easily implemented and are compatible with any popular deep learning architecture. Experimental results demonstrate the super capability of our algorithm in recognizing objects, where the scale and rotation changes occur frequently. The proposed GCNs have much fewer learnable network parameters, and thus is easier to train with an end-to-end pipeline. To encourage further developments, the source code is released at Github.


GaborNet: Gabor filters with learnable parameters in deep convolutional neural networks

The article describes a system for image recognition using deep convolut...

Circulant Binary Convolutional Networks: Enhancing the Performance of 1-bit DCNNs with Circulant Back Propagation

The rapidly decreasing computation and memory cost has recently driven t...

Orientation Convolutional Networks for Image Recognition

Deep Convolutional Neural Networks (DCNNs) are capable of obtaining powe...

Learnable Gabor modulated complex-valued networks for orientation robustness

Robustness to transformation is desirable in many computer vision tasks,...

Focal Sparse Convolutional Networks for 3D Object Detection

Non-uniformed 3D sparse data, e.g., point clouds or voxels in different ...

Deep Steganalysis: End-to-End Learning with Supervisory Information beyond Class Labels

Recently, deep learning has shown its power in steganalysis. However, th...

Local Unsupervised Learning for Image Analysis

Local Hebbian learning is believed to be inferior in performance to end-...