Deep Semi-supervised Learning


Research on Mean Teacher and Generative Adversarial Networks (GANs) based semi-supervised learning (SSL) …

  • Our present research is exploring the semi-supervised learning in the era of deep learning. Recently we made two significant contributions to address the confirmation bias and consistency issues in well-known deep learning models. The Mean Teacher (MT) model is known to suffer from confirmation bias, that is, reinforcing incorrect teacher model predictions. To address this problem, we developed a simple, yet effective method called Local Clustering (LC) to mitigate the effect of confirmation bias. In MT model, each data point is considered independent of other points during training; however, we note that data points are likely to be close to each other in feature space if they share similar features. Motivated by this observation, we cluster data points locally by minimizing the pairwise distance between neighboring data points in feature space. Combined with a standard classification cross-entropy objective on labeled data points, the misclassified unlabeled data points are pulled towards high-density regions of their correct class with the help of their neighbors, thus improving model performance. Thorough experiments on semi-supervised benchmark datasets SVHN and CIFAR-10 showed that adding our LC loss to MT yields significant improvements compared to MT and performance and comparable to the state of the art in semi-supervised learning.
  • Generative Adversarial Networks (GANs) based semi-supervised learning (SSL) approaches though shown to improve classification accuracy, their performance is lagging behind the state-of-the-art non-GAN based SSL approaches. We note that the lack of consistency in class probability predictions on the same image under local perturbations is one of the reasons for this performance gap. This problem was addressed in the past in a generic setting using the label consistency regularization, which enforces the class probability predictions for an input image to be unchanged under various semantic-preserving perturbations. We developed a new composite consistency regularization method in the framework of GAN based SSL and demonstrated the efficacy of new approach on two SSL image classification benchmark datasets, SVHN and CIFAR-10. Our experiments show that this new composite consistency regularization based semi-GAN significantly improves its performance and achieves new state-of-the-art performance among GAN-based SSL approaches.
    • Zexi Chen, Benjamin Dutton, Bharathkumar Ramachandra, Tianfu Wu, and Ranga Raju Vatsavai (2020): "Local Clustering with Mean Teacher for Semi-supervised learning." 25th International Conference on Pattern Recognition.
    • Zexi Chen, Bharathkumar Ramachandra, Ranga Raju Vatsavai (2020): Consistency Regularization with Generative Adversarial Networks for Semi-Supervised Image Classification. CoRR abs/2007.03844 (under review).