no code implementations • 25 Jan 2021 • Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe
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.
no code implementations • ICML Workshop LifelongML 2020 • Petru Soviany
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.
no code implementations • 15 Nov 2019 • Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe
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.
1 code implementation • 20 Oct 2019 • Petru Soviany, Claudiu Ardei, Radu Tudor Ionescu, Marius Leordeanu
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 #111 on Image Generation on CIFAR-10
no code implementations • 27 Nov 2018 • Petru Soviany, Radu Tudor Ionescu
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.
no code implementations • 23 Mar 2018 • Petru Soviany, Radu Tudor Ionescu
The image difficulty predictor is applied on the test images to split them into easy versus hard images.