[PDF] Top 20 Feedforward deep architectures for classification and synthesis
Has 10000 "Feedforward deep architectures for classification and synthesis" found on our website. Below are the top 20 most common "Feedforward deep architectures for classification and synthesis".
Feedforward deep architectures for classification and synthesis
... (x) and p g (x) to give itself a better-than-chance ability to correctly distinguish real and synthetic examples; the generator could then use the gradients obtained from this optimal discriminator to ... Voir le document complet
124
Efficient FPGA-Based Inference Architectures for Deep Learning Networks
... Computing and FPGAs (ReConFig) 2018 [11] Abstract–Deep Neural Networks (DNNs) have gained significant popularity in several classification and regression ...computation and memory ... Voir le document complet
117
Deep Transfer Learning for Art Classification Problems
... whether Deep Convolutional Neural Net- works (DCNNs), which have obtained state of the art results on the ImageNet challenge, are able to perform equally well on three different art classification ... Voir le document complet
16
Training Compact Deep Learning Models for Video Classification Using Circulant Matrices
... distillation and other model compression techniques begs an important question: is it possible to devise models that are compact by nature while exhibiting the same generalization properties as large ones? In ... Voir le document complet
14
A deep representation for invariance and music classification
... modules for building invariance to transformations and multiple layers for compositionality and ...modules for extracting invariant and discriminative audio ...hierarchical ... Voir le document complet
7
Deep learning for classification and severity estimation of coffee leaf biotic stress.
... using Deep Learning for the problems of biotic stress classification and severity estimation of the most important coffee diseases and pests through leaf ...images. For the accomplishment ... Voir le document complet
9
Deep Learning for Classification of Hyperspectral Data: A Comparative Review
... Deep Learning for Classification of Hyperspectral Data: A Comparative Review Nicolas Audebert, Bertrand Le Saux, Member, IEEE and S´ebastien Lef`evre Abstract—In recent years, deep ... Voir le document complet
14
Understanding deep architectures and the effect of unsupervised pre-training
... unaccounted for: in Figure ...particular for RBMs, which would not encode in their hidden layer any input bits that would be marginally independent of the others, because these bits would be explained by ... Voir le document complet
195
Training deep convolutional architectures for vision
... unit and a particular recent model of V1 response: a) the presence of a low-rank quadratic term in the argument to s, and b) the use of a gentler non-linearity than the tanh or logistic ...units and ... Voir le document complet
116
A Deep Representation for Invariance And Music Classification
... modules for building invariance to transformations and multiple layers for compositionality and ...modules for extracting invariant and discriminative audio ...hierarchical ... Voir le document complet
6
Leveraging deep neural networks with nonnegative representations for improved environmental sound classification
... DNN architectures can be sufficient to deal with ...NMF-DNN and TNMF-DNN systems is confirmed to out- perform the CQT-DNN ...allows for a 2%-accuracy ... Voir le document complet
7
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
... stage classification constitutes an important preliminary exam in the diagnosis of sleep ...(ECG) and electromyograms (EMG). We introduce here the first deep learning approach for sleep stage ... Voir le document complet
13
Transmitter Classification With Supervised Deep Learning
... supervised deep learning (SDL) has imposed itself as the tool to achieve state-of-the-art performance in many fields, starting with image processing to voice recognition, product suggestion, and more ... Voir le document complet
15
Do Convolutional Networks need to be Deep for Text Classification ?
... multiple deep models on many ...(Collobert and Weston, 2008) consisting of fine-tuned or fixed pretraining word2vec em- beddings (Mikolov et ...2013) and its combina- tion as ... Voir le document complet
12
Cross-modal interaction in deep neural networks for audio-visual event classification and localization
... ]. For example, the model, with the best result for the DCASE2019 challenge [ 177 ], was composed of four CRNN ...detected and the classification of the ...challenge. For example, Comminiello ... Voir le document complet
243
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
... exploit deep learning to analyze such kind of ...cover classification in the agricultural ...detection classification task ...approach for land cover classification on optical SITS ... Voir le document complet
33
RED-NN: Rotation-Equivariant Deep Neural Network for Classification and Prediction of Rotation
... the number of parameters (Table 1 , 2 ) allows to have all these capabilities on embedded or portable devices. Some tests on these devices are presented in the supplementary material. Our model can learn the internal ... Voir le document complet
12
Natural image processing and synthesis using deep learning
... composed deep feed-forward network, called domain-adversarial neural network (DANN) (il- lustrated by Figure ...layers and loss functions, and can be trained using standard backpropagation algorithms ... Voir le document complet
175
Deep neural networks for automatic classification of anesthetic-induced unconsciousness
... EEG classification [5 –8]. Raw Data Representation. For reducing the computational complexity of the deep learning pipeline, 20 electrodes of EEG data were examined, located as per the 10-20 system, ... Voir le document complet
11
Disease Classification in Metagenomics with 2D Embeddings and Deep Learning
... Ehrlich, and Oluf ...Li and A Godzik. Cd-hit: a fast pro- gram for clustering and comparing large sets of protein or nucleotide ...Liu and Jeffrey ... Voir le document complet
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