Deep Learning Optimized Dictionary Learning and Its Application in Eliminating Strong Magnetotelluric Noise

Greedy Deep Dictionary Learning

—In this work we propose a new deep learning tool – deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion – one layer at a time. This requires solving a simple (shallow) dictionary learning problem; the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like s...

Deep Learning With Noise

Recent works have shown that, by allowing some inaccuracy when training deep neural networks, not only the training performance but also the accuracy of the model can be improved. Our work, taking those previous works as examples and guidance, tries to study the impact of introducing different types of noise in different components of training a deep neural network. We intend to experiment with...

Deep Dictionary Learning: A PARametric NETwork Approach

Deep dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics. We propose a method for learning a hierarchy of synthesis dictionaries with an image classification goal. The dictionaries and classification parameters are trained by a classification objective, and the sparse features are extracted by reducing a reconstruction loss...

Active Labeling in Deep Learning and Its Application to Emotion

Adaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep Learning

Introduction: Electrocardiogram (ECG) is a method to measure the electrical activity of the heart which is performed by placing electrodes on the surface of the body. Physicians use observation tools to detect and diagnose heart diseases, the same is performed on ECG signals by cardiologists. In particular, heart diseases are recognized by examining the graphic representation of heart signals w...