Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets

• URL: http://arxiv.org/abs/2401.00575v1
• Date: Sun, 31 Dec 2023 19:25:34 GMT
• Title: Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets
• Authors: Payam Karisani
• Abstract summary: One of the weaknesses of self-training is the semantic drift problem. We reshape the role of pseudo-labels and create a hierarchical order of information. A crucial step in self-training is to use the confidence prediction to select the best candidate pseudo-labels.
• Score: 11.385682758047775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a semi-supervised text classifier based on self-training using one positive and one negative property of neural networks. One of the weaknesses of self-training is the semantic drift problem, where noisy pseudo-labels accumulate over iterations and consequently the error rate soars. In order to tackle this challenge, we reshape the role of pseudo-labels and create a hierarchical order of information. In addition, a crucial step in self-training is to use the classifier confidence prediction to select the best candidate pseudo-labels. This step cannot be efficiently done by neural networks, because it is known that their output is poorly calibrated. To overcome this challenge, we propose a hybrid metric to replace the plain confidence measurement. Our metric takes into account the prediction uncertainty via a subsampling technique. We evaluate our model in a set of five standard benchmarks, and show that it significantly outperforms a set of ten diverse baseline models. Furthermore, we show that the improvement achieved by our model is additive to language model pretraining, which is a widely used technique for using unlabeled documents. Our code is available at https://github.com/p-karisani/RST.

Related papers

• Co-training for Low Resource Scientific Natural Language Inference [65.37685198688538]
  We propose a novel co-training method that assigns weights based on the training dynamics of the classifiers to the distantly supervised labels. By assigning importance weights instead of filtering out examples based on an arbitrary threshold on the predicted confidence, we maximize the usage of automatically labeled data. The proposed method obtains an improvement of 1.5% in Macro F1 over the distant supervision baseline, and substantial improvements over several other strong SSL baselines.
  arXiv  Detail & Related papers  (2024-06-20T18:35:47Z)
• Boosting Semi-Supervised Learning by bridging high and low-confidence predictions [4.18804572788063]
  Pseudo-labeling is a crucial technique in semi-supervised learning (SSL) We propose a new method called ReFixMatch, which aims to utilize all of the unlabeled data during training.
  arXiv  Detail & Related papers  (2023-08-15T00:27:18Z)
• Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels [61.97359362447732]
  Learning from noisy labels is an important and long-standing problem in machine learning for real applications. In this paper, we reformulate the label-noise problem from a generative-model perspective. Our model achieves new state-of-the-art (SOTA) results on all the standard real-world benchmark datasets.
  arXiv  Detail & Related papers  (2023-05-31T03:01:36Z)
• NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning [8.749830466953584]
  Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive amounts of unlabeled data. In this work we introduce a new framework for SSL named NorMatch. We demonstrate, through numerical and visual results, that NorMatch achieves state-of-the-art performance on several datasets.
  arXiv  Detail & Related papers  (2022-11-17T15:39:18Z)
• Pseudo-Label Noise Suppression Techniques for Semi-Supervised Semantic Segmentation [21.163070161951868]
  Semi-consuming learning (SSL) can reduce the need for large labelled datasets by incorporating unsupervised data into the training. Current SSL approaches use an initially supervised trained model to generate predictions for unlabelled images, called pseudo-labels. We use three mechanisms to control pseudo-label noise and errors.
  arXiv  Detail & Related papers  (2022-10-19T09:46:27Z)
• Debiased Pseudo Labeling in Self-Training [77.83549261035277]
  Deep neural networks achieve remarkable performances on a wide range of tasks with the aid of large-scale labeled datasets. To mitigate the requirement for labeled data, self-training is widely used in both academia and industry by pseudo labeling on readily-available unlabeled data. We propose Debiased, in which the generation and utilization of pseudo labels are decoupled by two independent heads.
  arXiv  Detail & Related papers  (2022-02-15T02:14:33Z)
• Semi-Supervised Text Classification via Self-Pretraining [0.0]
  We present a semi-supervised learning model termed Self-Pretraining. Our model is inspired by the classic self-training algorithm. It can potentially update its belief about previously labeled documents.
  arXiv  Detail & Related papers  (2021-09-30T17:45:16Z)
• Cycle Self-Training for Domain Adaptation [85.14659717421533]
  Cycle Self-Training (CST) is a principled self-training algorithm that enforces pseudo-labels to generalize across domains. CST recovers target ground truth, while both invariant feature learning and vanilla self-training fail. Empirical results indicate that CST significantly improves over prior state-of-the-arts in standard UDA benchmarks.
  arXiv  Detail & Related papers  (2021-03-05T10:04:25Z)
• Two-phase Pseudo Label Densification for Self-training based Domain Adaptation [93.03265290594278]
  We propose a novel Two-phase Pseudo Label Densification framework, referred to as TPLD. In the first phase, we use sliding window voting to propagate the confident predictions, utilizing intrinsic spatial-correlations in the images. In the second phase, we perform a confidence-based easy-hard classification. To ease the training process and avoid noisy predictions, we introduce the bootstrapping mechanism to the original self-training loss.
  arXiv  Detail & Related papers  (2020-12-09T02:35:25Z)
• Uncertainty-aware Self-training for Text Classification with Few Labels [54.13279574908808]
  We study self-training as one of the earliest semi-supervised learning approaches to reduce the annotation bottleneck. We propose an approach to improve self-training by incorporating uncertainty estimates of the underlying neural network. We show our methods leveraging only 20-30 labeled samples per class for each task for training and for validation can perform within 3% of fully supervised pre-trained language models.
  arXiv  Detail & Related papers  (2020-06-27T08:13:58Z)

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.