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Found 11 papers, 8 papers with code
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data
Deep learning models have proven to be effective for this task by mapping time series data to high-level representation for prediction.
Ranked #1 on
Crop Classification
on CropHarvest - Brazil
Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications
In this work, we assess the impact of missing temporal and static EO sources in trained models across four datasets with classification and regression tasks.
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction
The GU module learned different weights based on the country and crop-type, aligning with the variable significance of each data source to the prediction task.
Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level
We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions.
A Comparative Assessment of Multi-view fusion learning for Crop Classification
Instead, we present a comparison of multi-view fusion methods for three different datasets and show that, depending on the test region, different methods obtain the best performance.
Ranked #2 on
Crop Classification
on CropHarvest - Togo
Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications
However, the approaches in the literature vary greatly since different terminology is used to refer to similar concepts or different illustrations are given to similar techniques.
On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
In addition, the S-VRAE𝑡 embeds the re-scaling preprocessing of the time series into the model in order to use the Flux standard deviation in the learning of the light curves structure.
Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing
This paper investigates the robustness of hashing methods based on variational autoencoders to the lack of supervision, focusing on two semi-supervised approaches currently in use.
Ranked #1 on
Supervised Image Retrieval
on CIFAR-10
Classical Machine Learning Techniques in the Search of Extrasolar Planets
The field of astronomical data analysis has experienced an important paradigm shift in the recent years.
Revisiting Machine Learning from Crowds a Mixture Model for Grouping Annotations
Handling this noise in a principled way is an important challenge for machine learning, called learning from crowds.
A Binary Variational Autoencoder for Hashing
Searching a large dataset to find elements that are similar to a sample object is a fundamental problem in computer science.
Ranked #1 on
Text Retrieval
on 20 Newsgroups
