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Found 5 papers, 2 papers with code
A geometry-aware deep network for depth estimation in monocular endoscopy
To address this issue, we introduce a gradient loss to penalize edge fluctuations ambiguous around stepped edge structures and a normal loss to explicitly express the sensitivity to frequently small structures, and propose a geometric consistency loss to spreads the spatial information across the sample grids to constrain the global geometric anatomy structures.
Attack Transferability Characterization for Adversarially Robust Multi-label Classification
We unveil how the transferability level of the attack determines the attackability of the classifier via establishing an information-theoretic analysis of the adversarial risk.
Characterizing the Evasion Attackability of Multi-label Classifiers
Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic.
Scientific Paper Recommendation: A Survey
The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation.
Prototypical Networks for Multi-Label Learning
We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative data embeddings distribute compactly to form a positive component and negative component respectively, while the positive component and negative component are pushed away from each other.
