Search Results for author:
Found 9 papers, 4 papers with code
Waveform-Domain Complementary Signal Sets for Interrupted Sampling Repeater Jamming Suppression
The interrupted-sampling repeater jamming (ISRJ) is coherent and has the characteristic of suppression and deception to degrade the radar detection capabilities.
Waveform-Domain Adaptive Matched Filtering for Suppressing Interrupted-Sampling Repeater Jamming
On this domain, an adaptive matched filtering model, known as the waveform-domain adaptive matched filtering (WD-AMF), is established to tackle the problem of ISRJ suppression without relying on a pre-existing ISRJ model.
Statistical Loss and Analysis for Deep Learning in Hyperspectral Image Classification
To overcome this problem, this work characterizes each class from the hyperspectral image as a statistical distribution and further develops a novel statistical loss with the distributions, not directly with samples for deep learning.
Deep Manifold Embedding for Hyperspectral Image Classification
Deep learning methods have played a more and more important role in hyperspectral image classification.
A novel statistical metric learning for hyperspectral image classification
In this paper, a novel statistical metric learning is developed for spectral-spatial classification of the hyperspectral image.
An End-to-End Joint Unsupervised Learning of Deep Model and Pseudo-Classes for Remote Sensing Scene Representation
Finally, joint learning of the pseudo-center loss and the pseudo softmax loss which is formulated with the samples and the pseudo labels is developed for unsupervised remote sensing scene representation to obtain discriminative representations from the scenes.
Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting
However, current single-stage detectors (e. g. SSD) have not presented competitive accuracy on common pedestrian detection benchmarks.
Ranked #11 on
Pedestrian Detection
on Caltech
(using extra training data)
Diversity in Machine Learning
Even though the diversity plays an important role in machine learning process, there is no systematical analysis of the diversification in machine learning system.
Binary Stereo Matching
In this paper, we propose a novel binary-based cost computation and aggregation approach for stereo matching problem.
