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Enhancing Self-Training Methods
Semi-supervised learning approaches train on small sets of labeled data along with large sets of unlabeled data.
Bottom-up Hierarchical Classification Using Confusion-based Logit Compression
In this work, we propose a method to efficiently compute label posteriors of a base flat classifier in the presence of few validation examples within a bottom-up hierarchical inference framework.
A Classification Refinement Strategy for Semantic Segmentation
Based on the observation that semantic segmentation errors are partially predictable, we propose a compact formulation using confusion statistics of the trained classifier to refine (re-estimate) the initial pixel label hypotheses.
Formal Concept Analysis of Rodent Carriers of Zoonotic Disease
The technique of Formal Concept Analysis is applied to a dataset describing the traits of rodents, with the goal of identifying zoonotic disease carriers, or those species carrying infections that can spillover to cause human disease.
