Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint Localization

• URL: http://arxiv.org/abs/2201.08613v2
• Date: Mon, 24 Jan 2022 11:04:47 GMT
• Title: Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint Localization
• Authors: Can Wang, Sheng Jin, Yingda Guan, Wentao Liu, Chen Qian, Ping Luo, Wanli Ouyang
• Abstract summary: Localizing keypoints of an object is a basic visual problem. Supervised learning of a keypoint localization network often requires a large amount of data. We propose to automatically select reliable pseudo-labeled samples with a series of dynamic thresholds.
• Score: 88.74813798138466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Localizing keypoints of an object is a basic visual problem. However, supervised learning of a keypoint localization network often requires a large amount of data, which is expensive and time-consuming to obtain. To remedy this, there is an ever-growing interest in semi-supervised learning (SSL), which leverages a small set of labeled data along with a large set of unlabeled data. Among these SSL approaches, pseudo-labeling (PL) is one of the most popular. PL approaches apply pseudo-labels to unlabeled data, and then train the model with a combination of the labeled and pseudo-labeled data iteratively. The key to the success of PL is the selection of high-quality pseudo-labeled samples. Previous works mostly select training samples by manually setting a single confidence threshold. We propose to automatically select reliable pseudo-labeled samples with a series of dynamic thresholds, which constitutes a learning curriculum. Extensive experiments on six keypoint localization benchmark datasets demonstrate that the proposed approach significantly outperforms the previous state-of-the-art SSL approaches.

Related papers

• (FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning [4.803231218533992]
  Federated Learning (FL) is a distributed machine learning framework that trains accurate global models while preserving clients' privacy-sensitive data. Most FL approaches assume that clients possess labeled data, which is often not the case in practice. We propose $(FL)2$, a robust training method for unlabeled clients using sharpness-aware consistency regularization.
  arXiv  Detail & Related papers  (2024-10-30T17:15:02Z)
• 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)
• Learning with Partial Labels from Semi-supervised Perspective [28.735185883881172]
  Partial Label (PL) learning refers to the task of learning from partially labeled data. We propose a novel PL learning method, namely Partial Label learning with Semi-Supervised Perspective (PLSP) PLSP significantly outperforms the existing PL baseline methods, especially on high ambiguity levels.
  arXiv  Detail & Related papers  (2022-11-24T15:12:16Z)
• Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning [57.436224561482966]
  Active learning is a research area that addresses the issues of expensive labeling by selecting the most important samples for labeling. We introduce a new diversity-based initial dataset selection algorithm to select the most informative set of samples for initial labeling in the active learning setting. Also, we propose a novel active learning query strategy, which uses diversity-based sampling on consistency-based embeddings.
  arXiv  Detail & Related papers  (2022-07-25T16:11:55Z)
• Few-shot Learning via Dependency Maximization and Instance Discriminant Analysis [21.8311401851523]
  We study the few-shot learning problem, where a model learns to recognize new objects with extremely few labeled data per category. We propose a simple approach to exploit unlabeled data accompanying the few-shot task for improving few-shot performance.
  arXiv  Detail & Related papers  (2021-09-07T02:19:01Z)
• Dash: Semi-Supervised Learning with Dynamic Thresholding [72.74339790209531]
  We propose a semi-supervised learning (SSL) approach that uses unlabeled examples to train models. Our proposed approach, Dash, enjoys its adaptivity in terms of unlabeled data selection.
  arXiv  Detail & Related papers  (2021-09-01T23:52:29Z)
• Self-Tuning for Data-Efficient Deep Learning [75.34320911480008]
  Self-Tuning is a novel approach to enable data-efficient deep learning. It unifies the exploration of labeled and unlabeled data and the transfer of a pre-trained model. It outperforms its SSL and TL counterparts on five tasks by sharp margins.
  arXiv  Detail & Related papers  (2021-02-25T14:56:19Z)
• 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)
• Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning [54.85397562961903]
  Semi-supervised learning (SSL) has been proposed to leverage unlabeled data for training powerful models when only limited labeled data is available. We address a more complex novel scenario named open-set SSL, where out-of-distribution (OOD) samples are contained in unlabeled data. Our method achieves state-of-the-art results by successfully eliminating the effect of OOD samples.
  arXiv  Detail & Related papers  (2020-07-22T10:33:55Z)

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.