Estimating time-frequency domain masks for single-channel speech enhancement using deep learning methods has recently become a popular research field with promising results. In this paper, we propose a novel components loss (CL) for the training of neural networks for mask-based speech enhancement. ...
Based on the mathematical principles of deep learning and the fully convolutional neural networks (FCNN), a deep fully convolutional neural network (d‐FCNN) model was constructed based on encoding and decoding methodology. Four model indicators, pixel accuracy (PA), average pixel accuracy (mPA),...
Here we show that a deep neural network can semantically segment RGB-D (i.e. color and depth) images into 13 building component classes simultaneously despite the use of a small training dataset with only 1490 object instances. For this task, the method achieves an average intersection over ...
Conventional machine learning models (e.g., support vector machine, random forest, Gaussian process regression, shallow neural network) and deep learning model (e.g., deep neural network) are used to predict the creep, fatigue and creep-fatigue failure life of 316 austenitic stainless steel. It...
论文地址:[2006.15920] Interpreting and Disentangling Feature Components of Various Complexity from DNNs (arxiv.org) 1 Intro Deep neural network have demostrated significant success in various tasks. 除了DNNs的优越性能外,近年来还有研究尝试DNNs的可解释性。以往对DNN的解释研究归纳为两类,即对DNN的事后解...
Result: [ 4. 9. 16.] Gradient of x: [4. 6. 8.] FunctionsIn Chainer Functions are operations that are applied to data within a neural network. These functions are essential building blocks that perform mathematical operations, activation functions, loss computations and other transformations on...
Component reference: Overview of all components,Platform For AI:This topic describes the components supported by Machine Learning Designer of Platform for AI (PAI).
The Internet of Things (IoT) environment has become the basic channel for the propagation of Distributed Denial of Service (DDoS) and malware intrusions. C
In recent years, the ability of deep neural network models to learn fault features of a large number of samples has been well known and widely used in the field of fault diagnosis [3, 4]. However, the success of deep learning-based fault diagnosis depends on the following two conditions:...
Optional - Deep Learning Internals Internally, the behavior data is first passed to a pre-processing function which converts the behavior coordinates into features to be trained on. A three layered fully connected neural network (FCNN) is used for training. The model is configured based on par...