Top PDF Training deep convolutional architectures for vision
Training deep convolutional architectures for vision
... of Convolutional Neural Networks (CNN), such as LeNet-5 [40] in arti- ficial vision tasks like hand-written digit classification or object recognition, stems from their architecture and inherent ...their ...
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Deep Background Subtraction with Scene-Specific Convolutional Neural Networks
... the training data while the second one is used as a test ...designed for static cameras and that videos of the intermittent object motion category does not fulfill our requirement about the ...
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Training Compact Deep Learning Models for Video Classification Using Circulant Matrices
... and Deep Learning that demonstrate how imposing a structure on large weight matrices can be used to reduce the size of the ...models for video classification based on state-of-the- art network ...
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Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction
... key for early warning and control management of air pollution, especially in emergency situations, where big amounts of pollutants are quickly released in the air, causing considerable ...multi-point deep ...
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Geodesic Convolutional Neural Network for 3D Deep-Learning based Surrogate Modeling and Optimization
... [3] Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein. Geometric deep learning on graphs and manifolds using mixture model cnns. In Pro- ceedings of the IEEE ...
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2020 — Modeling information flow through deep convolutional neural networks
... The Convolutional Neural Network The Convolutional Neural Network (CNN) has become recognized as the state of the art approach to many computer vision tasks including image-based object ...approach ...
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Technical report: supervised training of convolutional spiking neural networks with PyTorch
... a Deep Continuous Local Learning (DECOLLE) capable of learning deep spatio-temporal representations from spikes by approximating gradient backpropagation using locally syn- thesized ...loss for each ...
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Green Function and Electromagnetic Potential for Computer Vision and Convolutional Neural Network Applications
... and Vision Computing, Elsevier, December 2018 Abstract In recent years, there has been rapid progress in solving the binary problems in computer vision, such as edge detection which finds the boundaries of ...
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Deep Nets: What have they ever done for Vision?
... model for reading CAPTCHAs which factorize geometry and appearances, enabling the geometry and appearance to be learned separately, hence saving on training ...harder for a “black box” like a ...
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Report Transfer Learning of Deep Convolutional Network on Twitter
... same pool of 1.6M positive and negative Stanford data. Though this is not really a good approximation but it works quite well in the litteratures ([4], [11]). The loss of the precision is hoped to become less important ...
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Efficient FPGA-Based Inference Architectures for Deep Learning Networks
... the training, the validation and the testing processes of ...accelerator for NNs, but it does not support variable network size and ...scalable deep learning accelerator unit on ...
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Classification of Time-Series Images Using Deep Convolutional Neural Networks
... Learning. Training the above CNN architecture is similar to the ...model. For faster convergence, the stochastic gradient descent (SGD) is used for updating the ...The training phase has two ...
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A deep convolutional neural network for classification of red blood cells in sickle cell anemia
... folds for training and one fold for ...RBCs for training, which we arrange in 50 batches of 20 images each except the last one that has only 8 RBCs; see Fig 19A ...layer-by-layer. ...
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Auto-Encoders, Distributed Training and Information Representation in Deep Neural Networks
... enables training by (possibly regularized) maximum likelihood and gradient descent computed via simple back-propagation, avoiding the need to compute intractable partition ...supervised training tricks. ...
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Deep learning of representations and its application to computer vision
... allow for training of sufficiently large ...inspiration for connectionism, and view biological intelligence as a proof of concept giving some indication of what we can hope to achieve by simulating ...
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Are Topographic Deep Convolutional Neural Networks Better Models of the Ventral Visual Stream?
... position for each of the model units in “IT” layers (fc6 and fc7) on a two-dimensional artificial tissue map before training, simulating cortical maps in monkey IT (Figure ...high for nearby pairs ...
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Deep convolutional neural networks to monitor coralligenous reefs: Operationalizing biodiversity and ecological assessment
... room for improvement when applying CNNs to ecological ...of training such deep architectures, the networks used by most studies implementing CNNs for image classification were ...
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Predicting the Propagation of Acoustic Waves using Deep Convolutional Neural Networks
... whereas for training 𝑇 1 𝐶 some peaks with a 30% error relative to the 𝑟 𝑚𝑠 density value are found before 𝜏 = ...harder for a network that has not seen those patterns during ...
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Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates
... possibility for applications in many other fields, such as physics, where the causal nature of DL [7] suggests that complex patterns could also be sought and ...problems for which deterministic equations are ...
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Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates
... possibility for applications in many other fields, such as physics, where the causal nature of DL [7] suggests that complex patterns could also be sought and ...problems for which deterministic equations are ...
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