Semi-Supervised Learning

SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification

We propose a novel algorithm that focuses on the less studied relationship between the unlabeled data. Our algorithm achieves state-of-the-art performance on standard benchmarks

Learning with Less Labeling (LwLL)

We propose a novel algorithm for semi-supervised classification that achieves state-of-the-art performance on standard benchmarks and outperform previous works on transfer setting by a large margin.