Weakly Supervised Semantic and Attentive Data Mixing Augmentation for Fine-Grained Visual Categorization
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3163302
Abstract: As a key factor, the availability of large-scale training samples determines the improvement of visual performance. However, the size of Fine-Grained Visual Categorization (FGVC) datasets is always limited. Therefore, overfitting as an issue in FGVC-related… read more here.
Keywords: data mixing; training samples; training; mixing augmentation ... See more keywords
Robust Deep Active Learning via Distance-Measured Data Mixing and Adversarial Training
Sign Up to like & getrecommendations! Published in 2025 at "Entropy"
DOI: 10.3390/e27111159
Abstract: Accurate uncertainty estimation in unlabeled data represents a fundamental challenge in active learning. Traditional deep active learning approaches suffer from a critical limitation: uncertainty-based selection strategies tend to concentrate excessively around noisy decision boundaries, while… read more here.
Keywords: deep active; distance measured; measured data; active learning ... See more keywords