NP Match

NP-Match is a novel semi-supervised learning method leveraging neural processes (NPs) to improve image classification by effectively utilizing unlabeled data. Current research focuses on enhancing the efficiency and accuracy of NP-Match, particularly in handling imbalanced and multi-label datasets, and comparing its performance against existing uncertainty-based methods. This approach holds significance for reducing the reliance on large labeled datasets, a crucial limitation in many machine learning applications, and offers potential improvements in various computer vision tasks. Related research also explores the computational complexity of problems related to NP, investigating connections between probabilistic polynomial time and NP-complete problems.