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Found 8 papers, 0 papers with code
Generating Data Augmentation samples for Semantic Segmentation of Salt Bodies in a Synthetic Seismic Image Dataset
Our method uses deep learning models to generate pairs of seismic image patches and their respective salt masks for the Data Augmentation.
Prior Flow Variational Autoencoder: A density estimation model for Non-Intrusive Load Monitoring
We train and evaluate our proposed model in a publicly available dataset composed of power demand measures from a poultry feed factory located in Brazil.
SeismoFlow -- Data augmentation for the class imbalance problem
In this work, we propose the SeismoFlow a flow-based generative model to create synthetic samples, aiming to address the class imbalance.
Seismic Shot Gather Noise Localization Using a Multi-Scale Feature-Fusion-Based Neural Network
Herein, we describe the following: (1) the construction of a real-world dataset of seismic noise localization based on 6, 500 seismograms; (2) a multi-scale feature-fusion-based detector that uses the MobileNet combined with the Feature Pyramid Net as the backbone; and (3) the Single Shot multi-box detector for box classification/regression.
A Deep Convolutional Network for Seismic Shot-Gather Image Quality Classification
Deep Learning-based models such as Convolutional Neural Networks, have led to significant advancements in several areas of computing applications.
Cumulative Sum Ranking
Furthermore, we provide mistake bounds for each one of the two online algorithms.
A Multimodal CNN-based Tool to Censure Inappropriate Video Scenes
More than telling if a video is either appropriate or inappropriate, it is also important to identify which parts of it contain such content, for preserving parts that would be discarded in a simple broad analysis.
Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources
With the recent progress in machine learning, boosted by techniques such as deep learning, many tasks can be successfully solved once a large enough dataset is available for training.
