GrowingNet: An endtoend growing network for semisupervised learning Abstract   Semisupervised learning (SSL) typically involves a small quantity of labeled data and a large quantity of unlabeled data. As such, the successful application of semisupervised learning (SSL) depends on distinguishing easy and hard samples which contributes substantial recognition, as well as obtaining a more accurate pseudo target for a hard sample. However, existing SSL network models with deeper layers will suffer from overfitting or optimization difficulties. To address these problems, we propose a growing network (GrowingNet) where the convolution depth of the model can expand and contract. We also propose an incremental learning method by which the amount of pseudo labeled data can be increased uniformly. During training, the goal is to increase the convolutional layers of our model and the number of pseudo labeled data synchronously. We divide training epochs, the convolutional layers of GrowingNet, and pseudo labeled data into u equal parts. During each part of training epochs, we increase one part of convolutional layers, select one division of pseudo labeled data into the training process. The accuracy of the model will improve as training progresses, which distinguishes easy and hard samples and also provides more reliable pseudo labels during subsequent part of training epochs. This provides significant improvements over stateoftheart networks in most of cases on SSL benchmark tasks (CIFAR10, CIFAR100, and SVHN). Specifically, without data augmentation, our model produces error rates of 20.86%, 18.22%, and 12.02% on CIFAR10 with 1000, 2000, and 4000 labeled data, as well as error rates of 5.03% and 3.46% on SVHN with 500 and 1000 labeled data, respectively. With data augmentation, the error rate reaches 12.16% on CIFAR10 with 2000 labeled data and 31.06% on CIFAR100 with 10,000 labeled data.

Neural Embedding Propagation on Heterogeneous Networks Classification is one of the most important problems in machine learning. To address label scarcity, semisupervised learning (SSL) has been intensively studied over the past two decades, which mainly leverages data affinity modeled by networks. Label propagation (LP), however, as the most popular SSL technique, mostly only works on homogeneous networks with singletyped simple interactions. In this work, we focus on the more general and powerful heterogeneous networks, which accommodate multityped objects and links, and thus endure multityped complex interactions. Specifically, we propose neural embedding propagation (NEP), which leverages distributed embeddings to represent objects and dynamically composed modular networks to model their complex interactions. While generalizing LP as a simple instance, NEP is far more powerful in its natural awareness of different types of objects and links, and the ability to automatically capture their important interaction patterns. Further, we develop a series of efficient training strategies for NEP, leading to its easy deployment on realworld heterogeneous networks with millions of objects. With extensive experiments on three datasets, we comprehensively demonstrate the effectiveness, efficiency, and robustness of NEP compared with stateoftheart network embedding and SSL algorithms.