Filter combination learning for CNN model compression

Image Compression by Cnn

Iterative Low-Rank Approximation for CNN Compression

Deep convolutional neural networks contain tens of millions of parameters, making them impossible to work efficiently on embedded devices. We propose iterative approach of applying low-rank approximation to compress deep convolutional neural networks. Since classification and object detection are the most favored tasks for embedded devices, we demonstrate the effectiveness of our approach by co...

Towards CNN Map Compression for camera relocalisation

This paper presents a study on the use of Convolutional Neural Networks for camera relocalisation and its application to map compression. We follow state of the art visual relocalisation results and evaluate response to different data inputs – namely, depth, grayscale, RGB, spatial position and combinations of these. We use a CNN map representation and introduce the notion of CNN map compressio...

Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps

Recent developments in the field of robot grasping have shown great improvements in the grasp success rates when dealing with unknown objects. In this work we improve on one of the most promising approaches, the Grasp Quality Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset [15].We propose a new architecture for the GQ-CNN and describe practical improvements that increase...

Convolution with Logarithmic Filter Groups for Efficient Shallow CNN

In this paper, we propose a new logarithmic filter grouping which can capture the nonlinearity of filter distribution in shallow CNNs. Residual identity shortcut is incorporated with the filter grouping to enhance the performance of shallow networks. The proposed logarithmic filter grouping is evaluated using a compact CNN structure with logarithmic group convolution modules which are composed ...