Add to ORKGImage recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to incorrect mapping on hardware accelerators, which may lead to timing uncertainty and incorrect behavior. In addition, the increasing demand for optimal performance has led to progress towards the optimization of different neural network operations, such as operator fusion. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess the performance and impact of such optimizations, and explore their effectivene...
Deep Neural Networks (DNNs) have transformed the field of multimedia generation and recognition by r...
Deep learning frameworks are of particular note, as they have been shown to drastically alter the pe...
The success of deep image classification networks has been met with enthusiasm and investment from b...
Image recognition tasks typically use deep learning and require enormous processing power, thus rely...
Image recognition tasks typically use deep learning and require enormous processing power, thus rely...
Image recognition tasks typically use deep learning and require enormous processing power, thus rely...
Deep learning is overhauling a plethora of applications such as voice assistants, autonomous vehicle...
Deep neural networks for computer vision are deployed in increasingly safety-critical and socially-i...
This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed ...
Deep learning is a branch of machine learning that aims to extract multiple simple features from da...
We introduce several new datasets namely ImageNet-A/O and ImageNet-R as well as a synthetic environm...
In recent years by the popularization of AI, an increasing number of enterprises deployed machine le...
Reliable and robust evaluation methods are a necessary first step towards developing machine learnin...
Deep learning has been successful in computer vision in recent years. Deep learning models achieve s...
The resurgence of machine learning in various applications and it's inherent compute-intensive natur...
Deep Neural Networks (DNNs) have transformed the field of multimedia generation and recognition by r...
Deep learning frameworks are of particular note, as they have been shown to drastically alter the pe...
The success of deep image classification networks has been met with enthusiasm and investment from b...
Image recognition tasks typically use deep learning and require enormous processing power, thus rely...
Image recognition tasks typically use deep learning and require enormous processing power, thus rely...
Image recognition tasks typically use deep learning and require enormous processing power, thus rely...
Deep learning is overhauling a plethora of applications such as voice assistants, autonomous vehicle...
Deep neural networks for computer vision are deployed in increasingly safety-critical and socially-i...
This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed ...
Deep learning is a branch of machine learning that aims to extract multiple simple features from da...
We introduce several new datasets namely ImageNet-A/O and ImageNet-R as well as a synthetic environm...
In recent years by the popularization of AI, an increasing number of enterprises deployed machine le...
Reliable and robust evaluation methods are a necessary first step towards developing machine learnin...
Deep learning has been successful in computer vision in recent years. Deep learning models achieve s...
The resurgence of machine learning in various applications and it's inherent compute-intensive natur...
Deep Neural Networks (DNNs) have transformed the field of multimedia generation and recognition by r...
Deep learning frameworks are of particular note, as they have been shown to drastically alter the pe...
The success of deep image classification networks has been met with enthusiasm and investment from b...