61 citations · 164 across the 21 of their papers we have counts for
30 papers · 1 filter
Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation
Sukesh Adiga, Jose Dolz, Herve Lombaert
Semi-supervised segmentation tackles the scarcity of annotations by leveraging unlabeled data with a small amount of labeled data. A prominent way to utilize the unlabeled data is…
On the pitfalls of entropy-based uncertainty for multi-class semi-supervised segmentation
Martin Van Waerebeke, Gregory Lodygensky, Jose Dolz
Semi-supervised learning has emerged as an appealing strategy to train deep models with limited supervision. Most prior literature under this learning paradigm resorts to dual-base…
Mutual-Information Based Few-Shot Classification
Malik Boudiaf, Ziko Imtiaz Masud, Jérôme Rony +3
We introduce Transductive Infomation Maximization (TIM) for few-shot learning. Our method maximizes the mutual information between the query features and their label predictions fo…
Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!
Hoel Kervadec, Houda Bahig, Laurent Letourneau-Guillon +2
Standard losses for training deep segmentation networks could be seen as individual classifications of pixels, instead of supervising the global shape of the predicted segmentation…
Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains
Le Thanh Nguyen-Meidine, Atif Belal, Madhu Kiran +3
Beyond the complexity of CNNs that require training on large annotated datasets, the domain shift between design and operational data has limited the adoption of CNNs in many real-…
Bladder segmentation based on deep learning approaches: current limitations and lessons
Mark G. Bandyk, Dheeraj R Gopireddy, Chandana Lall +2
Precise determination and assessment of bladder cancer (BC) extent of muscle invasion involvement guides proper risk stratification and personalized therapy selection. In this cont…