Search Results for author:
Found 2 papers, 1 papers with code
Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification
Existing MIML methods are useful in many applications but most of which suffer from relatively low accuracy and training efficiency due to several issues: i) the inter-label correlations(i. e., the probabilistic correlations between the multiple labels corresponding to an object) are neglected; ii) the inter-instance correlations (i. e., the probabilistic correlations of different instances in predicting the object label) cannot be learned directly (or jointly) with other types of correlations due to the missing instance labels; iii) diverse inter-correlations (e. g., inter-label correlations, inter-instance correlations) can only be learned in multiple stages.
Two-stage Image Classification Supervised by a Single Teacher Single Student Model
The samples of the candidate classes are utilized to learn a student classifier based on L2-minimization in the second stage.
