Deep learning algorithms (in particular Convolutional Neural Networks, or CNNs) have shown their superiority in computer vision tasks and continue to push the state of the art in the most difficult problems of the field. However, deep models frequently lack interpretability. Current research efforts are often focused on increasingly complex and computationally expensive structures. These can be either handcrafted or generated by an algorithm, but in either case the specific choices of individual structural elements are hard to justify. This paper aims to analyze statistical properties of a large sample of small deep networks, where the choice of layer types is randomized. The limited representational power of such models forces them to special...
© 2016 IEEE. Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for var...
Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as a modelin...
Modern deep neural networks are highly over-parameterized compared to the data on which they are tra...
Understanding how feature learning affects generalization is among the foremost goals of modern deep...
Deep learning neural networks, or, more precisely, Convolutional Neural Networks (CNNs), have demons...
Convolutional Neural Networks (CNN) are the most popular of deep network models due to their applica...
Deep learning neural networks, or, more precisely, Convolutional Neural Networks (CNNs), have demons...
The influence of deep learning is continuously expanding across different domains, and its new appli...
We present an analysis of different techniques for selecting the connection be-tween layers of deep ...
The influence of deep learning is continuously expanding across different domains, and its new appli...
CNN Filter DB: An Empirical Investigation of Trained Convolutional. Poster as presented at CVPR2022...
Deep neural networks are representation learning techniques. During training, a deep net is capable ...
Deep neural networks are representation learning techniques. During training, a deep net is capable ...
Across scientific and engineering disciplines, the algorithmic pipeline forprocessing and understand...
Currently, many theoretical as well as practically relevant questions towards the transferability an...
© 2016 IEEE. Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for var...
Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as a modelin...
Modern deep neural networks are highly over-parameterized compared to the data on which they are tra...
Understanding how feature learning affects generalization is among the foremost goals of modern deep...
Deep learning neural networks, or, more precisely, Convolutional Neural Networks (CNNs), have demons...
Convolutional Neural Networks (CNN) are the most popular of deep network models due to their applica...
Deep learning neural networks, or, more precisely, Convolutional Neural Networks (CNNs), have demons...
The influence of deep learning is continuously expanding across different domains, and its new appli...
We present an analysis of different techniques for selecting the connection be-tween layers of deep ...
The influence of deep learning is continuously expanding across different domains, and its new appli...
CNN Filter DB: An Empirical Investigation of Trained Convolutional. Poster as presented at CVPR2022...
Deep neural networks are representation learning techniques. During training, a deep net is capable ...
Deep neural networks are representation learning techniques. During training, a deep net is capable ...
Across scientific and engineering disciplines, the algorithmic pipeline forprocessing and understand...
Currently, many theoretical as well as practically relevant questions towards the transferability an...
© 2016 IEEE. Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for var...
Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as a modelin...
Modern deep neural networks are highly over-parameterized compared to the data on which they are tra...