Semi-Supervised Learning with Balanced Deep Representation Distributions
Semi-Supervised Text Classification (SSTC) mainly works under the spirit of self-training. They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseudo-labels and train the deep classifier over the mixture of labeled and pseudo-labeled texts. Naturally, their performance is largely affected by the accuracy of pseudo-labels for unlabeled texts. Unfortunately, they often suffer from low accuracy because of the margin bias problem caused by the large difference between representation distributions of labels in SSTC. To alleviate this problem, we apply the angular margin loss, and perform several Gaussian linear transformat...
| Authors | Changchun Li, Ximing Li, Bingjie Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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