[PDF] Top 20 Deep neural networks with transfer learning in millet crop images
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Deep neural networks with transfer learning in millet crop images
... Pearl millet is one the most important food crop in Mali and ...tropical. Millet diseases are important items in the reduction of quality and quantity in crop ...disease ... Voir le document complet
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Classification of Time-Series Images Using Deep Convolutional Neural Networks
... Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw ...texture images and ... Voir le document complet
9
Deep Transfer Learning for Art Classification Problems
... Abstract. In this paper we investigate 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 ... Voir le document complet
16
Classification of Time-Series Images Using Deep Convolutional Neural Networks
... Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw ...texture images and ... Voir le document complet
9
Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks
... actions in a unknown video is still a challenging task in computer ...Recently, deep learn- ing algorithms has proved its great potential in many vision- related recognition ...tasks. ... Voir le document complet
7
Deep neural networks are lazy : on the inductive bias of deep learning
... or in other words, get the posterior 𝒫(𝑋|𝑌 ). In order to do this, we apply Bayes rule 𝒫(𝑋|𝑌 ) ∝ 𝒫(𝑌 |𝑋)𝒫(𝑋), where 𝒫(𝑋) is the ...research in the field of Bayesian statistics but the overall ... Voir le document complet
78
Comparing learned representations of deep neural networks
... features in in- put images, and have very high adversarial example ...desirable in certain use-cases where consistency is preferred, or particularly undesirable in avoiding adversarial ... Voir le document complet
64
Learning Sparse deep neural networks using efficient structured projections on convex constraints for green AI
... OTIVATION Deep neural networks have been applied recently to different domains and have shown a dramatic improvement in accuracy of image recognition [1], speech recognition [2] or natural ... Voir le document complet
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De-noising and de-blurring of images using deep neural networks
... In this paper, we test using a Learning Synthesis Deep Neural Network (LS-DNN) [2] in combination with BM3D [3], an off the shelf de-noising tool, to generate images, att[r] ... Voir le document complet
12
Multimodal and Crossmodal Representation Learning from Textual and Visual Features with Bidirectional Deep Neural Networks for Video Hyperlinking
... consecutive images (frames) of which the most meaningful ones are ...stored in the video ...that, in most videos, neighbouring frames contain a lot of redundant ...frame in the beginning, ... Voir le document complet
9
Automatic fault mapping in remote optical images and topographic data with deep learning
... ). In most computer vision tasks, CNNs indeed achieve state-of-the-art ...machine learning techniques to assist researchers in detecting, modeling, or predicting specific features in a broad ... Voir le document complet
39
Spiking Neural Networks modelled as Timed Automata with parameter learning
... Abstract In this paper we present a novel approach to automatically infer param- eters of spiking neural ...Spiking neural networks are formalised as sets of automata, one for each neuron, ... Voir le document complet
35
Entropy and mutual information in models of deep neural networks
... observe in our high-dimensional experiments that compression does happen during learning, even when using ReLU ...compression in our setting, there are many directions to be further explored within ... Voir le document complet
66
Extensive deep neural networks for transferring small scale learning to large scale systems
... EDNN in use We have demonstrated that EDNNs can be used to accurately learn the mapping from atomic coordinates to ...function in an actual simulation? To investigate this we per- formed a Metropolis Monte ... Voir le document complet
14
Unsupervised Layer-Wise Model Selection in Deep Neural Networks
... Abstract. Deep Neural Networks (DNN) propose a new and ef- ficient ML architecture based on the layer-wise building of several representation ...neurons in each DNN ...increases with ... Voir le document complet
7
DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning
... orchestration in a sliced ...listed in Table I, and study three practical case studies, where DeepCog is used to drive the orchestration at C-RAN, MEC and core datacenters, ...3 in this ... Voir le document complet
10
Aggregated learning: a vector quantization approach to learning with neural networks
... AgrLearn with two widely adopted NLP deep-learning architectures, CNN and LSTM [4], using two benchmark sentence-classification datasets, Movie Review [13] 3 and Subjectivity ...sentences, ... Voir le document complet
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Relating images and 3D models with convolutional neural networks
... real images Leveraging 3D models enables retrieval on real images for which the query object is very speci c and annotated photographs are not available or not easily ...dataset with balanced ... Voir le document complet
136
Deep neural networks for choice analysis
... challenges in the application of DNN to choice analysis, including the tension between domain-specific knowledge and generic-purpose mod- els, and the lack of interpretability and effective regularization ... Voir le document complet
128
Deep neural networks for audio scene recognition
... consists in determining automatically the context or environment around a device ...music. In this way, time- domain (zero-crossing rate), frequency-domain (band-energy ration, spectral centroid, spectral ... Voir le document complet
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