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[PDF] Top 20 Deep Learning for Image Memorability Prediction : the Emotional Bias

Has 10000 "Deep Learning for Image Memorability Prediction : the Emotional Bias" found on our website. Below are the top 20 most common "Deep Learning for Image Memorability Prediction : the Emotional Bias".

Deep Learning for Image Memorability Prediction : the Emotional Bias

Deep Learning for Image Memorability Prediction : the Emotional Bias

... predict image memorability ...on the ImageNet classification challenge have been achieved using CNN-based models [17, ...large image datasets with memorability ...paradigm for ... Voir le document complet

6

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning

... outperformed the baselines on both measures, with the L 0 model slightly outperforming L all , as expected for evaluating the pixel-level ...as the initial training set in order to ... Voir le document complet

19

Quality Assessment of Deep-Learning-Based Image Compression

Quality Assessment of Deep-Learning-Based Image Compression

... and deep-learning-based (Toderici et ...While for standard codecs the trends are similar as in previous studies [22], an analysis of the results in Table II reveals that the PCC ... Voir le document complet

7

Deep Learning vs. Kernel Methods: Performance for Emotion Prediction in Videos

Deep Learning vs. Kernel Methods: Performance for Emotion Prediction in Videos

... to the validation set and finally, 5,150 segments from 7 movies to the test ...that the genre of the movies in each set is as diverse as ...presents the results of using CNNs ... Voir le document complet

8

Effective and annotation efficient deep learning for image understanding

Effective and annotation efficient deep learning for image understanding

... On the other hand, for the residual based approaches it is easier to learn to predict zero residuals in the case of correct initial labels, but it is more difficult for them to refine ... Voir le document complet

236

Deep learning for clinical mammography screening

Deep learning for clinical mammography screening

... and deep learning, all the screening mammograms today are still read ...manually. Deep Learning techniques have revolutionized various fields such as object recog- nition, speech ... Voir le document complet

37

Assessing microscope image focus quality with deep learning

Assessing microscope image focus quality with deep learning

... training the model to predict on even more varied input images, including those spanning multiple spatial scales, additional imaging modalities such as brightfield, cell types, stains and pheno- ...accurate ... Voir le document complet

10

Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation

Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation

... reports the segmentation accuracies on three datasets respectively. The results show that our algorithm outperforms the methods MLRsubMLL [10], MPM-LBP- AL [41], 3D-CNN-LR [20] and WHED ...[14]. ... Voir le document complet

17

When Deep Learning Meets Digital Image Correlation

When Deep Learning Meets Digital Image Correlation

... In the present case of CNN however, no transfer function has been identified so far, so d can only be determined numerically or graphically, by seeking the intersection between the curve representing ... Voir le document complet

47

Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction

Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction

... a prediction module. The former is constructed by stacking multiple ConvLSTM ...layers, the values of each cell’s grid are determined by current and historical values of neighboring ...layers ... Voir le document complet

15

Deep learning for inter-observer congruency prediction

Deep learning for inter-observer congruency prediction

... to the literature regarding visual saliency, observers may exhibit considerable variations in their gaze ...in the observed images. The dispersion between the gaze of different observers ... Voir le document complet

6

Deep neural networks are lazy : on the inductive bias of deep learning

Deep neural networks are lazy : on the inductive bias of deep learning

... of the prior are the network architecture, the number of parameters, the non-linear activation functions, and the initialization ...Matter for Deep Learning As in ... Voir le document complet

78

Temporally downsampled cerebral CT perfusion image restoration using deep residual learning

Temporally downsampled cerebral CT perfusion image restoration using deep residual learning

... layers. The first 7 blocks in blue have 64-channel feature maps while the last block in red has 1-channel output, as shown in ...increase the training difficulty when the network becomes ... Voir le document complet

12

Deep Learning and Reinforcement Learning for Inventory Control

Deep Learning and Reinforcement Learning for Inventory Control

... to the end ...playing the game are very simple, the complex behavior of this dynamic system is ...interesting. The game is categorized in a group of games illustrating bullwhip effect (Devika ... Voir le document complet

69

Machine learning for image segmentation

Machine learning for image segmentation

... learning methods where features are mostly handcrafted, like the gPb algorithm, can be trained on smaller ...is for instance trained on the Berkeley Segmentation Dataset, which contains 200 ... Voir le document complet

155

Power of Prediction: Advantages of Deep Learning Modeling as Replacement for Traditional PUF CRP Enrollment

Power of Prediction: Advantages of Deep Learning Modeling as Replacement for Traditional PUF CRP Enrollment

... Aiming for a reliable, hence stable CRP set To prevent such challenges to take effect, a new technique should be used to replace traditional PUF ...of the PUF unit, instead of a huge CRP dataset (which we ... Voir le document complet

6

Deep learning investigation for chess player attention prediction using eye-tracking and game data

Deep learning investigation for chess player attention prediction using eye-tracking and game data

... CNN for visual attention prediction is deeply task dependent and thus is not a solved problem notably in chess ...context. The promising quantitative and qualitative result reported here show that ... Voir le document complet

16

Deep learning for cloud detection

Deep learning for cloud detection

... Experiments The main goal of this paper is to compare the perfor- mance of a ConvNet architecture applied to patches with the one obtained with a simple neural net with classical handcrafted ... Voir le document complet

8

Deep learning for cloud detection

Deep learning for cloud detection

... Abstract The SPOT 6-7 satellite ground segment includes a sys- tematic and automatic cloud detection step in order to feed a catalogue with a binary cloud mask and an appro- priate confidence ...approaches ... Voir le document complet

7

Deep Learning for Video Modelling

Deep Learning for Video Modelling

... in the line of hope for sequences RNN-like approaches to this ...alternative for UCF101, with ...is the net result of Conv3D, they compress the time ...on the time axis like it ... Voir le document complet

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