The ability to understand visual information from limited labeled data is an important aspect of machine learning. While image-level classification has been extensively studied in a semi-supervised setting, dense pixel-level with only drawn attention recently. In this work, we propose approach for semantic segmentation that learns pixel-wise annotated samples while exploiting additional annotat...

In recent years, semi-supervised graph learning with data augmentation (DA) has been the most commonly used and best-performing method to improve model robustness in sparse scenarios few labeled samples. However, of existing DA methods are based on homogeneous while none specific for heterogeneous graph. Differing from graph, faces greater challenges: heterogeneity information requires strategi...

We present a novel approach for training a multi-layered perceptron (MLP) in a semi-supervised fashion. Our objective function, when optimized, balances training set accuracy with fidelity to a graph-based manifold over all points. Additionally, the objective favors smoothness via an entropy regularizer over classifier outputs as well as straightforward 2 regularization. Our approach also scale...