Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering.
In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets.
#2 best model for Semi-Supervised Image Classification on SVHN, 1000 labels
Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence.
#11 best model for Image Classification on MNIST
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
SOTA for Graph Classification on IPC-lifted