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Found 6 papers, 1 papers with code
Curriculum Learning: A Survey
Training machine learning models in a meaningful order, from the easy samples to the hard ones, using curriculum learning can provide performance improvements over the standard training approach based on random data shuffling, without any additional computational costs.
Curriculum Learning with Diversity for Supervised Computer Vision Tasks
We consider this kind of difficulty metric to be better suited for solving general problems, as it is not based on certain task-dependent elements, but more on the context of each image.
Curriculum Self-Paced Learning for Cross-Domain Object Detection
To alleviate this problem, researchers proposed various domain adaptation methods to improve object detection results in the cross-domain setting, e. g. by translating images with ground-truth labels from the source domain to the target domain using Cycle-GAN.
Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)
All strategies are first based on ranking the training images by their difficulty scores, which are estimated by a state-of-the-art image difficulty predictor.
Ranked #108 on
Image Generation
on CIFAR-10
Continuous Trade-off Optimization between Fast and Accurate Deep Face Detectors
All the approaches are based on separating the test images in two batches, an easy batch that is fed to a faster face detector and a difficult batch that is fed to a more accurate yet slower detector.
Optimizing the Trade-off between Single-Stage and Two-Stage Object Detectors using Image Difficulty Prediction
The image difficulty predictor is applied on the test images to split them into easy versus hard images.
