14 citations · 23 across the 13 of their papers we have counts for
4 papers · 1 filter
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation
Iman Abbasnejad, Fabio Zambetta, Flora Salim +4
SCONE-GAN presents an end-to-end image translation, which is shown to be effective for learning to generate realistic and diverse scenery images. Most current image-to-image transl…
Progressive Feature Adjustment for Semi-supervised Learning from Pretrained Models
Hai-Ming Xu, Lingqiao Liu, Hao Chen +2
As an effective way to alleviate the burden of data annotation, semi-supervised learning (SSL) provides an attractive solution due to its ability to leverage both labeled and unlab…
Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup
Damien Teney, Jindong Wang, Ehsan Abbasnejad
Mixup is a highly successful technique to improve generalization of neural networks by augmenting the training data with combinations of random pairs. Selective mixup is a family o…
ProtoCon: Pseudo-label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-supervised Learning
Islam Nassar, Munawar Hayat, Ehsan Abbasnejad +2
Confidence-based pseudo-labeling is among the dominant approaches in semi-supervised learning (SSL). It relies on including high-confidence predictions made on unlabeled data as ad…