[PDF] Top 20 Lightweight material acquisition using deep learning
Has 10000 "Lightweight material acquisition using deep learning" found on our website. Below are the top 20 most common "Lightweight material acquisition using deep learning".
Lightweight material acquisition using deep learning
... the acquisition of geometries and materials directly from real world examples, but this often comes at the cost of complex hardware and calibration ...on lightweight material appearance capture to ... Voir le document complet
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Active Clothing Material Perception Using Tactile Sensing and Deep Learning
... Clothing Material Perception using Tactile Sensing and Deep Learning Wenzhen Yuan 1 , Yuchen Mo 1,2 , Shaoxiong Wang 1 , and Edward ...category using their properties, and an ... Voir le document complet
9
Material acquisition using deep learning
... A selection of results from our one image method on real-world photographs. In each image pair, the left image is a photograph of a surface, and the right image is a re- rendering with environment lighting of the ... Voir le document complet
2
Pushing the limits of optical information storage using deep learning
... read-out as function of training set size N . each dot represents a single measurement. All datasets are characterized by a very good separation of the differ- ent spectra in the t-SNE plots, which explains why the ANN ... Voir le document complet
14
Rethinking deep active learning: Using unlabeled data at model training
... Active learning typically focuses on training a model on few labeled examples alone, while unlabeled ones are only used for ...by using both labeled and unlabeled data during model training across active ... Voir le document complet
13
Frankenstein: Learning Deep Face Representations using Small Data
... synthesize material images under different viewing and lighting conditions based on detailed surface geometry measurements, and use these to train a recognition system using a SIFT-VLAD representation ... Voir le document complet
12
Optimization and passive flow control using single-step deep reinforcement learning
... the material derivative term in (24)) that imposes to march the adjoint equations backwards in ...100 using the method documented in [20] (solving first the Navier–Stokes equations, writing all time steps ... Voir le document complet
28
Deep learning on attributed graphs
... an open question; one way could be to adapt the graph structure according to object distances or attention scores, which is an application of graph editing discussed below. Graph Editing. Graph generation is a very ... Voir le document complet
128
Learning Chaotic and Stochastic Dynamics from Noisy and Partial Observation using Variational Deep Learning
... II. E XPERIMENT AND RESULT As illustration of the proposed framework, we first consider an application to the identification of an ODE representation given noisy and irregularly sampled ob- servations, here for Lorenz-63 ... Voir le document complet
2
Looking for Mimicry in a Snake Assemblage Using Deep Learning
... makes their identification challenging when the geographic origin is unknown, even for experts (Geniez 2015). In com- parison, a previous DCNN-based analysis of species rec- ognition using 579,184 unstandardized ... Voir le document complet
14
Quantitative follow-up of pulmonary diseases using deep learning models
... representation using CNNs It has been demonstrated the effectiveness of CNNs applied to object ...a Deep Convolutional Neural Network was presented to create artistic images of high perceptual ... Voir le document complet
188
Improving Pedestrian Recognition using Incremental Cross Modality Deep Learning
... incremental-cross deep learning modality improves the pedestrian recognition ...VGG-16 using default and optimized learning settings based on an incremental cross-modality learning ... Voir le document complet
7
Cross-species analysis of enhancer logic using deep learning
... a deep learning (DL) model on the human ATAC-seq ...peaks using cisTopic —a probabilistic framework to analyze scATAC-seq data that can also be applied to boot- strapped bulk ATAC-seq data (Bravo ... Voir le document complet
22
Emulation of wildland fire spread simulation using deep learning
... inputs are derived from the simulation parameter inputs of Table 1. Conv: Convolution 2D; BN: Batch Normalization; AvgPool: Average Pooling 2D... simulated burned surface area, correspon[r] ... Voir le document complet
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Deep learning et authentification des textes
... du deep learning, aucun texte jusqu’ici ne s’est trouvé assez original et indépendant pour échapper complètement à l’attraction qu’exerce un écrivain sur tout ce qu’il ...du deep learning ... Voir le document complet
30
Reparametrization in deep learning
... for deep latent variable models as it involves computing for instance the expectation pθ,X (x) = E z∼pZ (pθ,X|Z(x | z) ...them. Learning using this approximate posterior is enabled by the use of the ... Voir le document complet
132
A framework for remote sensing images processing using deep learning techniques
... Considering a process object implementing such op- erations, it must propagate requested regions of im- ages to its inputs. In the following, we introduce a generic description of size and spacing modifications that a ... Voir le document complet
7
3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation
... Universit´ e Cˆ ote d’Azur, Inria, France 2 CREATIS, CNRS UMR 5220, INSERM U1206, France Abstract. We present a novel automated method to segment the my- ocardium of both left and right ventricles in MRI volumes. The ... Voir le document complet
8
Deep learning based registration using spatial gradients and noisy segmentation labels
... Abstract. Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based ap- proaches became quite popular, providing fast and performing ... Voir le document complet
6
Source localization in reverberant rooms using Deep Learning and microphone arrays
... machine learning approach for the sound source localization task, using raw multichannel ...time, using a neural network which mainly consists in successive learnable filterbanks based on residual ... Voir le document complet
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