Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection

• URL: http://arxiv.org/abs/2409.11653v2
• Date: Mon, 28 Oct 2024 10:50:50 GMT
• Title: Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection
• Authors: Qian Shao, Jiangrui Kang, Qiyuan Chen, Zepeng Li, Hongxia Xu, Yiwen Cao, Jiajuan Liang, Jian Wu,
• Abstract summary: Semi-Supervised Learning (SSL) has become a preferred paradigm in many deep learning tasks. We observe that how to select samples for labelling also significantly impacts performance, particularly under extremely low-budget settings. By adopting a modified Frank-Wolfe algorithm to minimise a novel criterion $alpha$-Maximum Mean Discrepancy ($alpha$-MMD), RDSS samples a representative subset for annotation from the unlabeled data.
• Score: 3.9620215314408984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-Supervised Learning (SSL) has become a preferred paradigm in many deep learning tasks, which reduces the need for human labor. Previous studies primarily focus on effectively utilising the labelled and unlabeled data to improve performance. However, we observe that how to select samples for labelling also significantly impacts performance, particularly under extremely low-budget settings. The sample selection task in SSL has been under-explored for a long time. To fill in this gap, we propose a Representative and Diverse Sample Selection approach (RDSS). By adopting a modified Frank-Wolfe algorithm to minimise a novel criterion $\alpha$-Maximum Mean Discrepancy ($\alpha$-MMD), RDSS samples a representative and diverse subset for annotation from the unlabeled data. We demonstrate that minimizing $\alpha$-MMD enhances the generalization ability of low-budget learning. Experimental results show that RDSS consistently improves the performance of several popular SSL frameworks and outperforms the state-of-the-art sample selection approaches used in Active Learning (AL) and Semi-Supervised Active Learning (SSAL), even with constrained annotation budgets.

Related papers

• Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
  We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios. We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples. Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-06-07T11:37:45Z)
• Querying Easily Flip-flopped Samples for Deep Active Learning [63.62397322172216]
  Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is. This paper proposes the it least disagree metric (LDM) as the smallest probability of disagreement of the predicted label.
  arXiv  Detail & Related papers  (2024-01-18T08:12:23Z)
• An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models [55.01592097059969]
  Supervised finetuning on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities. Active learning is effective in identifying useful subsets of samples to annotate from an unlabeled pool. We propose using experimental design to circumvent the computational bottlenecks of active learning.
  arXiv  Detail & Related papers  (2024-01-12T16:56:54Z)
• Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection [35.924633625147365]
  Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL) In this work, we investigate an active learning approach for ICL, where there is a limited budget for annotating examples. We propose a model-adaptive optimization-free algorithm, termed AdaICL, which identifies examples that the model is uncertain about.
  arXiv  Detail & Related papers  (2023-10-30T22:03:55Z)
• On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning [15.276356824489431]
  We propose Contrastive with Out-of-distribution (OOD) data for Long-Tail learning (COLT) We empirically identify the counter-intuitive usefulness of OOD samples in SSL long-tailed learning. Our method significantly improves the performance of SSL on long-tailed datasets by a large margin.
  arXiv  Detail & Related papers  (2023-06-08T04:32:10Z)
• Active Learning Principles for In-Context Learning with Large Language Models [65.09970281795769]
  This paper investigates how Active Learning algorithms can serve as effective demonstration selection methods for in-context learning. We show that in-context example selection through AL prioritizes high-quality examples that exhibit low uncertainty and bear similarity to the test examples.
  arXiv  Detail & Related papers  (2023-05-23T17:16:04Z)
• Active Learning at the ImageNet Scale [43.595076693347835]
  In this work, we study a combination of active learning (AL) and pretraining (SSP) on ImageNet. We find that performance on small toy datasets is not representative of performance on ImageNet due to the class imbalanced samples selected by an active learner. We propose Balanced Selection (BASE), a simple, scalable AL algorithm that outperforms random sampling consistently.
  arXiv  Detail & Related papers  (2021-11-25T02:48:51Z)
• Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning [101.28281124670647]
  Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data. We propose a novel training mechanism that could effectively exploit the presence of OOD data for enhanced feature learning. Our approach substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2021-08-12T09:14:44Z)
• A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification [38.68079253627819]
  Our benchmark consists of two fine-grained classification datasets obtained by sampling classes from the Aves and Fungi taxonomy. We find that recently proposed SSL methods provide significant benefits, and can effectively use out-of-class data to improve performance when deep networks are trained from scratch. Our work suggests that semi-supervised learning with experts on realistic datasets may require different strategies than those currently prevalent in the literature.
  arXiv  Detail & Related papers  (2021-04-01T17:59:41Z)
• Reducing Confusion in Active Learning for Part-Of-Speech Tagging [100.08742107682264]
  Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. We study the problem of selecting instances which maximally reduce the confusion between particular pairs of output tags. Our proposed AL strategy outperforms other AL strategies by a significant margin.
  arXiv  Detail & Related papers  (2020-11-02T06:24:58Z)

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.