This means it can process 1 image per millisecond for inference and 4 images per millisecond for learning. Its hardware and recent libraries are also faster, making it one of the fastest convnet utilities. Caffe powers start-up prototypes, academic research projects, and large-scale industrial ap...
DEEP LEARNING TESTING 优质文献 相似文献 同作者Survey on identification and prediction of security threats using various deep learning models on software testing In this research, authors give a literature analysis of the methods used to detect and anticipate security risks in software testing by using...
In the past few years, deep reinforcement learning (DRL) was proposed as an advanced model of RL in which DL is applied as an effective tool to enhance the learning rate for RL models. The achieved experiences are stored during the real-time learning process, whereas the generated data for ...
Nevertheless, using DL systems in safety- and security-critical applications requires to provide testing evidence for their dependable operation. Recent research in this direction focuses on adapting testing criteria from traditional software engineering as a means of increasing confidence for their correct...
learning through Batch-RL and models the state-action value function with a Graph Neural Network. We apply DRIFT to testing the Windows 10 operating system and show that DRIFT can robustly trigger the desired software functionality in a fully automated manner. Our experiments test the abili...
in this survey is on the use of Deep Learning-based methods. Deep Learning based methods dominate this research area by providing automatic feature engineering, the capability of dealing with large datasets, enabling the mining of features from limited data samples, and supporting one-shot learning...
Deep learning (DL) becomes increasingly pervasive, being used in a wide range of software applications. These software applications, named as DL based software (in short as DL software), integrate DL models trained using a large data corpus with DL programs written based on DL frameworks such ...
1, in the last five years, 3d face research has grown with every passing year. Most of the reconstruction research has preferred using GAN-based deep learning techniques. This paper aims to study 3D face reconstruction using deep learning techniques and their applications in a real-life scenario...
This paper is organized as follows. Section “Why Deep Learning in Today's Research and Applications?” motivates why deep learning is important to build data-driven intelligent systems. In Section“Deep Learning Techniques and Applications”, we present our DL taxonomy by taking into account the ...
Deep learning is not only fully developed in the fields of large language modeling and computer vision, but also in the field of communication. There are more and more scholars conducting in-depth research on it [48]. In the communication domain, deep learning has been applied mainly in the...