Add to ORKGA recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks. There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional method, which we call smart augmentation and we show how to use it to increase the accuracy and reduce over fitting on a target network. Smart augmentation works, by creating a network that learns how to generate augmented data during the training process of a target network in a way that reduces that networks loss. This allows us to learn augmentations that minimize the error of that network. Smart augmentation has shown the potentia...
Data augmentation is widely used as a part of the training process applied to deep learning models, ...
Artificial Intelligence on the edge is a matter of great importance towards the enhancement of smart...
Modern consumer electronic devices have adopted deep learning-based intelligence services for their ...
A recurring problem faced when training neural networks is that there is typically not enough data t...
In recent years, deep learning has revolutionized computer vision and has been applied to a range of...
Deep Learning for embedded vision requires large datasets. Indeed the more varied training data is, ...
In the recent years deep learning has become more and more popular and it is applied in a variety o...
To expand the size of a real dataset, data augmentation techniques artificially create various versi...
Currently deep learning requires large volumes of training data to fit accurate models. In practice,...
The data augmentation technique is used to increase the number of images in an image bank for traini...
Currently deep learning requires large volumes of training data to fit accurate models. In practice,...
Deep neural networks (DNNs) are widely used by both academic and industry researchers to solve many ...
To ensure good performance, modern machine learning models typically require large amounts of qualit...
In recent years, machine learning has very much been a prominent talking point, and is considered by...
In this era, machine learning and deep learning has become very ubiquitous and dominant in our socie...
Data augmentation is widely used as a part of the training process applied to deep learning models, ...
Artificial Intelligence on the edge is a matter of great importance towards the enhancement of smart...
Modern consumer electronic devices have adopted deep learning-based intelligence services for their ...
A recurring problem faced when training neural networks is that there is typically not enough data t...
In recent years, deep learning has revolutionized computer vision and has been applied to a range of...
Deep Learning for embedded vision requires large datasets. Indeed the more varied training data is, ...
In the recent years deep learning has become more and more popular and it is applied in a variety o...
To expand the size of a real dataset, data augmentation techniques artificially create various versi...
Currently deep learning requires large volumes of training data to fit accurate models. In practice,...
The data augmentation technique is used to increase the number of images in an image bank for traini...
Currently deep learning requires large volumes of training data to fit accurate models. In practice,...
Deep neural networks (DNNs) are widely used by both academic and industry researchers to solve many ...
To ensure good performance, modern machine learning models typically require large amounts of qualit...
In recent years, machine learning has very much been a prominent talking point, and is considered by...
In this era, machine learning and deep learning has become very ubiquitous and dominant in our socie...
Data augmentation is widely used as a part of the training process applied to deep learning models, ...
Artificial Intelligence on the edge is a matter of great importance towards the enhancement of smart...
Modern consumer electronic devices have adopted deep learning-based intelligence services for their ...