Learn to Accumulate Evidence from All Training Samples: Theory and Practice
Evidential deep learning, built upon belief theory and subjective logic, offers a principled and computationally efficient way to turn a deterministic neural network uncertainty-aware. The...
Multidimensional Belief Quantification for Label-Efficient Meta-Learning
Optimization-based meta-learning offers a promising direction for few-shot learning that is essential for many real-world computer vision applications. However, learning from few samples...