Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification?

• URL: http://arxiv.org/abs/2111.04357v2
• Date: Tue, 9 Nov 2021 16:49:05 GMT
• Title: Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification?
• Authors: Inigo V. Slijepcevic, Anna M. M. Scaife
• Abstract summary: We test whether SSL can achieve performance comparable to the current supervised state of the art when using many fewer labelled data points. We find that although SSL provides additional regularisation, its performance degrades rapidly when using very few labels.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we examine the robustness of state-of-the-art semi-supervised learning (SSL) algorithms when applied to morphological classification in modern radio astronomy. We test whether SSL can achieve performance comparable to the current supervised state of the art when using many fewer labelled data points and if these results generalise to using truly unlabelled data. We find that although SSL provides additional regularisation, its performance degrades rapidly when using very few labels, and that using truly unlabelled data leads to a significant drop in performance.

Related papers

• Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix [59.55173022987071]
  We study the potential of semi-supervised learning for class-agnostic motion prediction. Our framework adopts a consistency-based self-training paradigm, enabling the model to learn from unlabeled data. Our method exhibits comparable performance to weakly and some fully supervised methods.
  arXiv  Detail & Related papers  (2023-12-13T09:32:50Z)
• Can semi-supervised learning use all the data effectively? A lower bound perspective [58.71657561857055]
  We show that semi-supervised learning algorithms can leverage unlabeled data to improve over the labeled sample complexity of supervised learning algorithms. Our work suggests that, while proving performance gains for SSL algorithms is possible, it requires careful tracking of constants.
  arXiv  Detail & Related papers  (2023-11-30T13:48:50Z)
• Semi-Supervised Learning in the Few-Shot Zero-Shot Scenario [14.916971861796384]
  Semi-Supervised Learning (SSL) is a framework that utilizes both labeled and unlabeled data to enhance model performance. We propose a general approach to augment existing SSL methods, enabling them to handle situations where certain classes are missing. Our experimental results reveal significant improvements in accuracy when compared to state-of-the-art SSL, open-set SSL, and open-world SSL methods.
  arXiv  Detail & Related papers  (2023-08-27T14:25:07Z)
• Complementing Semi-Supervised Learning with Uncertainty Quantification [6.612035830987296]
  We propose a novel unsupervised uncertainty-aware objective that relies on aleatoric and epistemic uncertainty quantification. Our results outperform the state-of-the-art results on complex datasets such as CIFAR-100 and Mini-ImageNet.
  arXiv  Detail & Related papers  (2022-07-22T00:15:02Z)
• Self Supervised Learning for Few Shot Hyperspectral Image Classification [57.2348804884321]
  We propose to leverage Self Supervised Learning (SSL) for HSI classification. We show that by pre-training an encoder on unlabeled pixels using Barlow-Twins, a state-of-the-art SSL algorithm, we can obtain accurate models with a handful of labels.
  arXiv  Detail & Related papers  (2022-06-24T07:21:53Z)
• Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift [0.0]
  State-of-the-art semi-supervised learning algorithm applied to morphological classification of radio galaxies. We test if SSL with fewer labels can achieve test accuracies comparable to the supervised state-of-the-art. Improvement is limited to a narrow range of label volumes, with performance falling off rapidly at low label volumes.
  arXiv  Detail & Related papers  (2022-04-19T11:38:22Z)
• Robust Deep Semi-Supervised Learning: A Brief Introduction [63.09703308309176]
  Semi-supervised learning (SSL) aims to improve learning performance by leveraging unlabeled data when labels are insufficient. SSL with deep models has proven to be successful on standard benchmark tasks. However, they are still vulnerable to various robustness threats in real-world applications.
  arXiv  Detail & Related papers  (2022-02-12T04:16:41Z)
• Self-supervised Learning is More Robust to Dataset Imbalance [65.84339596595383]
  We investigate self-supervised learning under dataset imbalance. Off-the-shelf self-supervised representations are already more robust to class imbalance than supervised representations. We devise a re-weighted regularization technique that consistently improves the SSL representation quality on imbalanced datasets.
  arXiv  Detail & Related papers  (2021-10-11T06:29:56Z)
• In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning [53.1047775185362]
  Pseudo-labeling (PL) is a general SSL approach that does not have this constraint but performs relatively poorly in its original formulation. We argue that PL underperforms due to the erroneous high confidence predictions from poorly calibrated models. We propose an uncertainty-aware pseudo-label selection (UPS) framework which improves pseudo labeling accuracy by drastically reducing the amount of noise encountered in the training process.
  arXiv  Detail & Related papers  (2021-01-15T23:29:57Z)
• Matching Distributions via Optimal Transport for Semi-Supervised Learning [31.533832244923843]
  Semi-Supervised Learning (SSL) approaches have been an influential framework for the usage of unlabeled data. We propose a new approach that adopts an Optimal Transport (OT) technique serving as a metric of similarity between discrete empirical probability measures. We have evaluated our proposed method with state-of-the-art SSL algorithms on standard datasets to demonstrate the superiority and effectiveness of our SSL algorithm.
  arXiv  Detail & Related papers  (2020-12-04T11:15:14Z)
• Boosting the Performance of Semi-Supervised Learning with Unsupervised Clustering [10.033658645311188]
  We show that ignoring labels altogether for whole epochs intermittently during training can significantly improve performance in the small sample regime. We demonstrate our method's efficacy in boosting several state-of-the-art SSL algorithms.
  arXiv  Detail & Related papers  (2020-12-01T14:19:14Z)

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.