81 citations · 140 across the 7 of their papers we have counts for
4 papers · 1 filter
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
Shell Xu Hu, Da Li, Jan Stühmer +2
Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated meta-learning…
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut
Yangtao Wang, Xi Shen, Shell Hu +3
Transformers trained with self-supervised learning using self-distillation loss (DINO) have been shown to produce attention maps that highlight salient foreground objects. In this…
Boosting Co-teaching with Compression Regularization for Label Noise
Yingyi Chen, Xi Shen, Shell Xu Hu +1
In this paper, we study the problem of learning image classification models in the presence of label noise. We revisit a simple compression regularization named Nested Dropout. We…
Variational Information Distillation for Knowledge Transfer
Sungsoo Ahn, Shell Xu Hu, Andreas Damianou +2
Transferring knowledge from a teacher neural network pretrained on the same or a similar task to a student neural network can significantly improve the performance of the student n…