Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
arXiv:2010.05517
Abstract
Unlabeled data learning has attracted considerable attention recently. However, it is still elusive to extract the expected high-level semantic feature with mere unsupervised learning. In the meantime, semi-supervised learning (SSL) demonstrates a promising future in leveraging few samples. In this paper, we combine both to propose an Unsupervised Semantic Aggregation and Deformable Template Matching (USADTM) framework for SSL, which strives to improve the classification performance with few labeled data and then reduce the cost in data annotating. Specifically, unsupervised semantic aggregation based on Triplet Mutual Information (T-MI) loss is explored to generate semantic labels for unlabeled data. Then the semantic labels are aligned to the actual class by the supervision of labeled data. Furthermore, a feature pool that stores the labeled samples is dynamically updated to assign proxy labels for unlabeled data, which are used as targets for cross-entropy minimization. Extensive experiments and analysis across four standard semi-supervised learning benchmarks validate that USADTM achieves top performance (e.g., 90.46 accuracy on CIFAR-10 with 40 labels and 95.20 accuracy with 250 labels). The code is released at https://github.com/taohan10200/USADTM.
arXiv commentaccepted by NeurIPS 2020
References in corpus (14)
- Improved Regularization of Convolutional Neural Networks with Cutout
- FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
- Learning deep representations by mutual information estimation and maximization
- Unsupervised Data Augmentation for Consistency Training
- Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
- Temporal Ensembling for Semi-Supervised Learning
- Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
- Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
- Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
- Learning Discrete Representations via Information Maximizing Self-Augmented Training
- ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
- Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
- Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation
- Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization