![salient edge map in keras data augmentation salient edge map in keras data augmentation](https://venturebeat.com/wp-content/uploads/2020/05/dd86bfe9-18cc-47f9-8276-4fae06dc60b9.png)
We demonstrate this by using AtmoDist to define a metric for GAN-based super resolution of vorticity and divergence. The task forces the network to learn important intrinsic aspects of the data as activations in its layers and from these hence a discriminative metric can be obtained. the components of the wind field from reanalysis or simulation). Our approach, called AtmoDist, trains a neural network on a simple, auxiliary task: predicting the temporal distance between elements of a randomly shuffled sequence of atmospheric fields (e.g. in computer vision, we present a novel, self-supervised representation learning approach specifically designed for atmospheric dynamics. Motivated by the success of intermediate neural network activations as basis for learned metrics, e.g. However, this ``eyeball metric'' cannot be used for machine learning where an algorithmic description is required. For atmospheric dynamics, a critical part of the climate system, no well established metric exists and visual inspection is currently still often used in practice. Machine learning and hybrid techniques for this prediction rely on informative metrics that are sensitive to pertinent but often subtle influences. Towards Representation Learning for Atmospheric DynamicsĪbstract: The prediction of future climate scenarios under anthropogenic forcing is critical to understand climate change and to assess the impact of potentially counter-acting technologies. Our approaches achieve superior performance.Īuthors: Hoang Chuong Nguyen (Australia National University) Miaomiao Liu (The Australian National University)Ĭomputer vision and remote sensing Power and energy We evaluate our approaches on benchmark datasets and demonstrate that Sky images in a deterministic as well as stochastic manner. We proposed deep neural networks to predict the future To enable the autoregressive prediction capability of the model, We therefore introduce approaches toĭeterministic and stochastic predictions to capture the most likelyĪs well as the diverse future of the solar irradiance.
![salient edge map in keras data augmentation salient edge map in keras data augmentation](https://www.edge-ai-vision.com/wp-content/uploads/2020/12/2020_Summit_EvanJuras_Fundamentals-768x432.jpg)
The future solar irradiance is naturally diverse over a relatively While it is likely deterministic for intra-hourly prediction, Work aims for the prediction of the most likely future of the solar (upto 4-hour ahead-of-time prediction) from a past sky image sequence. This paper focuses on short-term solar irradiance forecasting Solar power into the power grid system while maintaining its stability. Short-term Solar Irradiance Prediction from Sky ImagesĪbstract: Solar irradiance forecasting is essential for the integration of the Unsupervised and semi-supervised learning Disaster prediction, management, and relief Earth science and monitoring Computer vision and remote sensing
#Salient edge map in keras data augmentation code
Our method, which we release with all the code including trained models, can also be used as an open science benchmark for the Sentinel-1 released dataset.Īuthors: Siddha Ganju (Nvidia Corporation) Sayak Paul (Carted) Our approach sets a high score, and a new state-of-the-art on the Sentinel-1 dataset for the ETCI competition with 0.7654 IoU, an impressive improvement over the 0.60 IOU baseline. Additionally, we post process our results with Conditional Random Fields.
![salient edge map in keras data augmentation salient edge map in keras data augmentation](https://ars.els-cdn.com/content/image/1-s2.0-S1076633218303878-gr4.jpg)
This cyclical process is repeated until the performance improvement plateaus. This assimilated dataset is used for the next round of training ensemble models. Concretely, we use a cyclical approach involving multiple stages (1) training an ensemble model of multiple U-Net architectures with the provided high confidence hand-labeled data and, generated pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combine the generated labels with the previously available high confidence hand-labeled dataset.
![salient edge map in keras data augmentation salient edge map in keras data augmentation](https://d1m75rqqgidzqn.cloudfront.net/wp-data/2020/07/23141111/iStock-1141842182.jpg)
We propose a semi-supervised learning pseudo-labeling scheme that derives confidence estimates from U-Net ensembles, thereby progressively improving accuracy. The NASA Impact Emerging Techniques in Computational Intelligence (ETCI) competition on Flood Detection tasked participants with predicting flooded pixels after training with synthetic aperture radar (SAR) images in a supervised setting. Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised LearningĪbstract: Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies.