Ruben BroekxinSuperlinearRepresentation Learning Breakthroughs: Improved Generalization in Supervised ModelsLeveraging multi-scale crops in supervised models for superior generalization and performance across various tasks. #ICLR2023Sep 25, 2023Sep 25, 2023
Ruben BroekxinSuperlinearRepresentation Learning Breakthroughs: Convolutional Neural Networks Can Overfit Input SizeAddressing CNNs’ input size overfitting with spatially-balanced pooling for improved robustness. #ICLR2023Sep 18, 2023Sep 18, 2023
Ruben BroekxinSuperlinearRepresentation Learning Breakthroughs: Token Merging: Your ViT, but FasterBoosting ViT efficiency with Token Merging: significant speed boost, minimal loss in accuracy. #ICLR2023Sep 11, 2023Sep 11, 2023
Ruben BroekxinSuperlinearRepresentation Learning Breakthroughs: What is Representation Learning?Discover representation learning: a shift from manual feature engineering to automatic, efficient data interpretation in AI. #ICLR2023Sep 5, 2023Sep 5, 2023
Ruben BroekxinSuperlinearRefining a Crop Classification Model: From Data Augmentation to Model CalibrationPushing the boundaries in Remote Sensing for crop classificationJul 18, 2023Jul 18, 2023
Ruben BroekxinSuperlinearHow to Create a Pixel-Based Crop Classification ModelTechniques and Architectures for Remote Sensing and Agricultural ApplicationsMay 31, 2023May 31, 2023
Ruben BroekxinSuperlinearBanishing the Jitters: Stabilizing Satellite Imagery with OpenCV’s Phase CorrelationWorking with satellite imagery becomes more difficult the longer you have to analyze a specific patch of land over time. This is because…Apr 17, 20231Apr 17, 20231
