[PDF] Top 20 The potential of synthetic training data for training deep learning models
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The potential of synthetic training data for training deep learning models
... simulated data as training data in deep neural networks is a reliable training method when working with image ...real data as well as on simulated ...real data is compared ... See full document
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Large-Scale Distributed Training Applied to Generative Adversarial Networks for Calorimeter Simulation
... ing deep learning to solve typical tasks related to high energy physics data tak- ing and ...ronment. Training of neural network models has been made tractable with the improvement of ... See full document
8
Arbitrary View Action Recognition via Transfer Dictionary Learning on Synthetic Training Data
... the synthetic video could affect the classification accuracy of the ...human models instead of simplified cylinder- based models as in previous ...creating synthetic 2D videos, our current ... See full document
15
Deep Learning with Minimal Training Data: TurkuNLP Entry in the BioNLP Shared Task 2016
... During training, the pre-trained word embeddings are fine-tuned while randomly initialized POS and dependency type representations are trained from ...proposed deep neural network can be effi- ciently ... See full document
9
Why Does Unsupervised Pre-training Help Deep Learning?
... to learning algorithms for deep architectures such as Deep Belief Networks and stacks of auto-encoder variants, with impressive results obtained in several areas, mostly on vision and language ... See full document
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A SURVEY ON DEEP LEARNING TECHNIQUES, APPLICATIONS AND CHALLENGES
... machine learning is the lack of adequate training data to build accurate and reliable models in many realistic ...quality data are in short supply, the resulting models can ... See full document
7
Detection and analysis of wheat spikes using Convolutional Neural Networks
... a deep learning approach to accurately detect, count and analyze wheat spikes for yield ...different models were generated based on four different datasets of training and testing images ... See full document
13
Application of Deep Learning for 3D building generalization
... a data basis for the cartographic generalization and the preparation of training data, the 3D city model of Stuttgart, Germany, was ...building models has very detailed floor plans and roof ... See full document
8
DeepCRISPR: optimized CRISPR guide RNA design by deep learning
... same training and testing ...as Deep- CRISPR used in testing scenario 3, and also kept the test- ing data ...shallow models; therefore, the retrained sgRNA designer achieved better performance ... See full document
18
Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data
... such models, we perform approximate inference using several schedules for belief propagation, including tree-based repa- rameterization ...for training DCRFs: marginal likelihood training, for when ... See full document
31
CNN Classification Approach For Analysis And Recognition Of Marathi Manuscript
... shape data, it may take it ...model training, it is necessary to build up the learnable ...poor learning proportion/rate or improper scaling of the ...performance. Models adapted the Kera are ... See full document
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Training deep learning models to count based on synthetic data
... of deep learning has advanced enormously. Deep learning enables training of a neural network or model, which is capable of learning relations in data at a level that ... See full document
50
On the utilization of deep and ensemble learning to detect milk adulteration
... and deep learners were evaluated for adulterant detection on milk sam- ...of training and testing described earlier for GBM, RF, and CNN ...our models are shown in Table ...the training set ... See full document
13
Text Classification Using Ensemble Of Non Linear Support Vector Machines
... There exist various variants of SVMs[14]-[19]. These are the state-of-the-art SVM models that have been used for text classification. Wang et al[15] propose a fuzzy SVM based approach for text classification. They ... See full document
6
Spatio-Temporal Image Representation of 3D Skeletal Movements for View-Invariant Action Recognition with Deep Convolutional Neural Networks
... spaced data in the time domain, applying S-G filter on raw skeletal data helps reduce the level of noise while maintaining the 3D geometric characteristics of the input ... See full document
25
Training Automatic Transliteration Models on DBPedia Data
... Moses models and some simple heuristics to detect if transliteration is appro- priate, and to perform it if it ...our models would be somewhat differ- ent than manual transliteration, one could still make ... See full document
9
Mining Stack Overflow: a Recommender Systems-Based Model
... Rafi et al. [12] proposed an automated approach for finding and ranking potential relevant classes for bug reports. Their approach used a multi- objective optimization algorithm to find balance between minimizing ... See full document
11
Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLP
... Network models (DNNs) now dom- inate the NLP ...When training DNNs, even simple baselines can take a lot of time and resources to reach peak per- formance (Melis et ...new models is lack- ...new ... See full document
7
Deep Active Learning for Named Entity Recognition
... used deep neural networks (DNNs) to advance the state of the art in named entity recognition (NER) (Collobert et ...of deep learning have been less pronounced when work- ing with small ... See full document
5
Data Driven Analysis and Prediction using Regression Models on Iot based Drainage Monitoring System
... In most developing countries the drainage system has posed several problems to the environment and posing a threat to human life especially to the municipality workers. In the light of this issue this paper has ... See full document
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