1 citations · 1 across the 1 of their papers we have counts for
5 papers
Curriculum Learning with Diversity for Supervised Computer Vision Tasks
Petru Soviany
Curriculum learning techniques are a viable solution for improving the accuracy of automatic models, by replacing the traditional random training with an easy-to-hard strategy. How…
Curriculum Self-Paced Learning for Cross-Domain Object Detection
Petru Soviany, Radu Tudor Ionescu, Paolo Rota +1
Training (source) domain bias affects state-of-the-art object detectors, such as Faster R-CNN, when applied to new (target) domains. To alleviate this problem, researchers proposed…
Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)
Petru Soviany, Claudiu Ardei, Radu Tudor Ionescu +1
Despite the significant advances in recent years, Generative Adversarial Networks (GANs) are still notoriously hard to train. In this paper, we propose three novel curriculum learn…
Continuous Trade-off Optimization between Fast and Accurate Deep Face Detectors
Petru Soviany, Radu Tudor Ionescu
Although deep neural networks offer better face detection results than shallow or handcrafted models, their complex architectures come with higher computational requirements and sl…
Optimizing the Trade-off between Single-Stage and Two-Stage Object Detectors using Image Difficulty Prediction
Petru Soviany, Radu Tudor Ionescu
There are mainly two types of state-of-the-art object detectors. On one hand, we have two-stage detectors, such as Faster R-CNN (Region-based Convolutional Neural Networks) or Mask…