Semi-Supervised Learning with Balanced Deep Representation Distributions

• URL: http://arxiv.org/abs/2603.21056v1
• Date: Sun, 22 Mar 2026 04:46:58 GMT
• Title: Semi-Supervised Learning with Balanced Deep Representation Distributions
• Abstract summary: We propose a novel Semi-Supervised Text Classification with Balanced Deep representation Distributions (S2TC-BDD)<n>We implement both multi-class classification and multi-label classification versions of S2TC-BDD by introducing some pseudo-labeling tricks and regularization terms.<n> Empirical results demonstrate the effectiveness of S2 TC-BDD, especially when the labeled texts are scarce.
• Score: 18.02420981397051
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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 transformations to achieve balanced label angle variances, i.e., the variance of label angles of texts within the same label. More accuracy of predicted pseudo-labels can be achieved by constraining all label angle variances balanced, where they are estimated over both labeled and pseudo-labeled texts during self-training loops. With this insight, we propose a novel SSTC method, namely Semi-Supervised Text Classification with Balanced Deep representation Distributions (S2TC-BDD). We implement both multi-class classification and multi-label classification versions of S2TC-BDD by introducing some pseudo-labeling tricks and regularization terms. To evaluate S2 TC-BDD, we compare it against the state-of-the-art SSTC methods. Empirical results demonstrate the effectiveness of S2 TC-BDD, especially when the labeled texts are scarce.

Related papers

• Boosting Meta-Learning for Few-Shot Text Classification via Label-guided Distance Scaling [14.128153594493964]
  Few-shot text classification aims to recognize unseen classes with limited labeled text samples.<n>We propose a textbfLabel-guided textbfDistance textbfScaling (LDS) strategy.<n>In the training stage, we design a label-guided loss to inject label semantic information, pulling closer the sample representations.<n>In the testing stage, we propose a Label-guided Scaler which scales sample representations with label semantics to provide additional supervision signals.
  arXiv  Detail & Related papers  (2026-02-28T13:59:15Z)
• DiCaP: Distribution-Calibrated Pseudo-labeling for Semi-Supervised Multi-Label Learning [83.94574004953346]
  Semi-supervised multi-label learning aims to leverage unlabeled data to improve the model's performance.<n>Most existing methods assign equal weights to all pseudo-labels regardless of their quality.<n>We propose Distribution-Calibrated Pseudo-labeling (DiCaP), a correctness-aware framework that estimates posterior precision to calibrate pseudo-label weights.
  arXiv  Detail & Related papers  (2025-11-25T11:55:02Z)
• Prompt-based Pseudo-labeling Strategy for Sample-Efficient Semi-Supervised Extractive Summarization [12.582774521907227]
  Semi-supervised learning (SSL) is a widely used technique in scenarios where labeled data is scarce and unlabeled data is abundant. Standard SSL methods follow a teacher-student paradigm to first train a classification model and then use the classifier's confidence values to select pseudo-labels. We propose a prompt-based pseudo-labeling strategy with LLMs that picks unlabeled examples with more accurate pseudo-labels.
  arXiv  Detail & Related papers  (2023-11-16T04:29:41Z)
• Generating Unbiased Pseudo-labels via a Theoretically Guaranteed Chebyshev Constraint to Unify Semi-supervised Classification and Regression [57.17120203327993]
  threshold-to-pseudo label process (T2L) in classification uses confidence to determine the quality of label. In nature, regression also requires unbiased methods to generate high-quality labels. We propose a theoretically guaranteed constraint for generating unbiased labels based on Chebyshev's inequality.
  arXiv  Detail & Related papers  (2023-11-03T08:39:35Z)
• JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification [65.268245109828]
  Semi-supervised text classification (SSTC) has gained increasing attention due to its ability to leverage unlabeled data. Existing approaches based on pseudo-labeling suffer from the issues of pseudo-label bias and error accumulation. We propose JointMatch, a holistic approach for SSTC that addresses these challenges by unifying ideas from recent semi-supervised learning.
  arXiv  Detail & Related papers  (2023-10-23T05:43:35Z)
• Rank-Aware Negative Training for Semi-Supervised Text Classification [3.105629960108712]
  Semi-supervised text classification-based paradigms (SSTC) typically employ the spirit of self-training. This paper presents a Rank-aware Negative Training (RNT) framework to address SSTC in learning with noisy label manner.
  arXiv  Detail & Related papers  (2023-06-13T08:41:36Z)
• Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective [89.5370481649529]
  We propose a label distribution perspective for PU learning in this paper. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions. Experiments on three benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-12-06T07:38:29Z)
• Acknowledging the Unknown for Multi-label Learning with Single Positive Labels [65.5889334964149]
  Traditionally, all unannotated labels are assumed as negative labels in single positive multi-label learning (SPML) We propose entropy-maximization (EM) loss to maximize the entropy of predicted probabilities for all unannotated labels. Considering the positive-negative label imbalance of unannotated labels, we propose asymmetric pseudo-labeling (APL) with asymmetric-tolerance strategies and a self-paced procedure to provide more precise supervision.
  arXiv  Detail & Related papers  (2022-03-30T11:43:59Z)
• CLS: Cross Labeling Supervision for Semi-Supervised Learning [9.929229055862491]
  Cross Labeling Supervision ( CLS) is a framework that generalizes the typical pseudo-labeling process. CLS allows the creation of both pseudo and complementary labels to support both positive and negative learning.
  arXiv  Detail & Related papers  (2022-02-17T08:09:40Z)
• Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning [80.05441565830726]
  This paper addresses imbalanced semi-supervised learning, where heavily biased pseudo-labels can harm the model performance. We propose a general pseudo-labeling framework to address the bias motivated by this observation. We term the novel pseudo-labeling framework for imbalanced SSL as Distribution-Aware Semantics-Oriented (DASO) Pseudo-label.
  arXiv  Detail & Related papers  (2021-06-10T11:58:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.