To aggregate diverse learners and to train deep architectures are the two principal avenues towards increasing the expressive capabilities of neural networks. Therefore, their combinations merit attention. In this contribution, we study how to apply some conventional diversity methods-bagging and label switching- to a general deep machine, the stacked denoising auto-encoding classifier, in order to solve a number of appropriately selected image recognition problems. The main conclusion of our work is that binarizing multi-class problems is the key to obtain benefit from those diversity methods. Additionally, we check that adding other kinds of performance improvement procedures, such as pre-emphasizing training samples and elastic distortio...
Abstract—Deep architectures have been used in transfer learning applications, with the aim of improv...
Deep artificial neural networks require a large corpus of training data in order to effectively lear...
Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural n...
To aggregate diverse learners and to train deep architectures are the two principal avenues towards ...
One of the most recent area in the Machine Learning research is Deep Learning. Deep Learning algorit...
Abstract—Transfer learning is a process that allows reusing a learning machine trained on a problem ...
We investigate unsupervised pre-training of deep architectures as feature genera-tors for “shallow ”...
© 2017 IEEE. Deep networks have achieved excellent performance in learning representation from visua...
14th International Work-Conference on Artificial Neural Networks, IWANN 2017Machine ensembles are le...
We examine one profound learning technique named stacked denoising autoencoder (SDA). SDA stacks a f...
Traditional artificial neural architectures possess limited ability to address the scale problem exh...
In the last five years, deep learning methods and particularly Convolutional Neural Networks (CNNs) ...
Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural n...
Part 6: Classification Pattern RecognitionInternational audienceDeep learning is promising approach ...
Recent studies on multi-label image classification have focused on designing more complex architect...
Abstract—Deep architectures have been used in transfer learning applications, with the aim of improv...
Deep artificial neural networks require a large corpus of training data in order to effectively lear...
Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural n...
To aggregate diverse learners and to train deep architectures are the two principal avenues towards ...
One of the most recent area in the Machine Learning research is Deep Learning. Deep Learning algorit...
Abstract—Transfer learning is a process that allows reusing a learning machine trained on a problem ...
We investigate unsupervised pre-training of deep architectures as feature genera-tors for “shallow ”...
© 2017 IEEE. Deep networks have achieved excellent performance in learning representation from visua...
14th International Work-Conference on Artificial Neural Networks, IWANN 2017Machine ensembles are le...
We examine one profound learning technique named stacked denoising autoencoder (SDA). SDA stacks a f...
Traditional artificial neural architectures possess limited ability to address the scale problem exh...
In the last five years, deep learning methods and particularly Convolutional Neural Networks (CNNs) ...
Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural n...
Part 6: Classification Pattern RecognitionInternational audienceDeep learning is promising approach ...
Recent studies on multi-label image classification have focused on designing more complex architect...
Abstract—Deep architectures have been used in transfer learning applications, with the aim of improv...
Deep artificial neural networks require a large corpus of training data in order to effectively lear...
Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural n...