Search Results for author:
Found 3 papers, 0 papers with code
Calibrating Deep Neural Networks by Pairwise Constraints
This suggests the hypothesis that DNN calibration can be improved by providing calibration supervision to all such binary problems.
Learning Deep Classifiers Consistent With Fine-Grained Novelty Detection
The problem of novelty detection in fine-grained visual classification (FGVC) is considered.
Learning with Bounded Instance- and Label-dependent Label Noise
Inspired by the idea of learning with distilled examples, we then propose a learning algorithm with theoretical guarantees for its robustness to BILN.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
