Add to ORKGIn the recent years deep learning has become more and more popular and it is applied in a variety of fields, yielding outstanding results in different machine learning applications. Deep learning based solutions thrive when a large amount of data is available for a specific problem but data availability and preparation are the biggest bottlenecks in the deep learning pipelines. With the fast-changing technology environment, new unique problems arise daily. In order to realise solutions in many of these specific problem domains there is a growing need to build custom datasets that are tailored for a particular use case with matching ground truth data. Acquiring such datasets at the scale required for training with today’s AI systems ...
To train a deep learning (DL) model, considerable amounts of data are required to generalize to unse...
Within modern Deep Learning setups, data augmentation is the weapon of choice when dealing with narr...
Recent research shows that Data Augmentation techniques and Synthetic Data can improve the accuracy ...
In recent years, deep learning has revolutionized computer vision and has been applied to a range of...
Deep artificial neural networks require a large corpus of training data in order to effectively lear...
Deep Learning for embedded vision requires large datasets. Indeed the more varied training data is, ...
Deep learning allows computers to learn from observations, or else training data. Successful applica...
Deep learning has achieved remarkable results in many computer vision tasks. Deep neural networks ty...
To ensure good performance, modern machine learning models typically require large amounts of qualit...
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amoun...
Currently deep learning requires large volumes of training data to fit accurate models. In practice,...
A recurring problem faced when training neural networks is that there is typically not enough data t...
The advent of data mining and machine learning has highlighted the value of large and varied sources...
Currently deep learning requires large volumes of training data to fit accurate models. In practice,...
As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learn...
To train a deep learning (DL) model, considerable amounts of data are required to generalize to unse...
Within modern Deep Learning setups, data augmentation is the weapon of choice when dealing with narr...
Recent research shows that Data Augmentation techniques and Synthetic Data can improve the accuracy ...
In recent years, deep learning has revolutionized computer vision and has been applied to a range of...
Deep artificial neural networks require a large corpus of training data in order to effectively lear...
Deep Learning for embedded vision requires large datasets. Indeed the more varied training data is, ...
Deep learning allows computers to learn from observations, or else training data. Successful applica...
Deep learning has achieved remarkable results in many computer vision tasks. Deep neural networks ty...
To ensure good performance, modern machine learning models typically require large amounts of qualit...
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amoun...
Currently deep learning requires large volumes of training data to fit accurate models. In practice,...
A recurring problem faced when training neural networks is that there is typically not enough data t...
The advent of data mining and machine learning has highlighted the value of large and varied sources...
Currently deep learning requires large volumes of training data to fit accurate models. In practice,...
As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learn...
To train a deep learning (DL) model, considerable amounts of data are required to generalize to unse...
Within modern Deep Learning setups, data augmentation is the weapon of choice when dealing with narr...
Recent research shows that Data Augmentation techniques and Synthetic Data can improve the accuracy ...