Towards Realistic Semi-Supervised Learning

• URL: http://arxiv.org/abs/2207.02269v1
• Date: Tue, 5 Jul 2022 19:04:43 GMT
• Title: Towards Realistic Semi-Supervised Learning
• Authors: Mamshad Nayeem Rizve, Navid Kardan, Mubarak Shah
• Abstract summary: We propose a novel approach to tackle SSL in open-world setting, where we simultaneously learn to classify known and unknown classes. Our approach substantially outperforms the existing state-of-the-art on seven diverse datasets.
• Score: 73.59557447798134
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data. Recently, ORCA [9] introduce a more realistic SSL problem, called open-world SSL, by assuming that the unannotated data might contain samples from unknown classes. This work proposes a novel approach to tackle SSL in open-world setting, where we simultaneously learn to classify known and unknown classes. At the core of our method, we utilize sample uncertainty and incorporate prior knowledge about class distribution to generate reliable pseudo-labels for unlabeled data belonging to both known and unknown classes. Our extensive experimentation showcases the effectiveness of our approach on several benchmark datasets, where it substantially outperforms the existing state-of-the-art on seven diverse datasets including CIFAR-100 (17.6%), ImageNet-100 (5.7%), and Tiny ImageNet (9.9%).

Related papers

• Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition [50.61991746981703]
  Current state-of-the-art LTSSL approaches rely on high-quality pseudo-labels for large-scale unlabeled data. This paper introduces a novel probabilistic framework that unifies various recent proposals in long-tail learning. We introduce a continuous contrastive learning method, CCL, extending our framework to unlabeled data using reliable and smoothed pseudo-labels.
  arXiv  Detail & Related papers  (2024-10-08T15:06:10Z)
• Robust Semi-Supervised Learning for Self-learning Open-World Classes [5.714673612282175]
  In real-world applications, unlabeled data always contain classes not present in the labeled set. We propose an open-world SSL method for Self-learning Open-world Classes (SSOC), which can explicitly self-learn multiple unknown classes. SSOC outperforms the state-of-the-art baselines on multiple popular classification benchmarks.
  arXiv  Detail & Related papers  (2024-01-15T09:27:46Z)
• FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning [73.13448439554497]
  Semi-Supervised Learning (SSL) has been an effective way to leverage abundant unlabeled data with extremely scarce labeled data. Most SSL methods are commonly based on instance-wise consistency between different data transformations. We propose FlatMatch which minimizes a cross-sharpness measure to ensure consistent learning performance between the two datasets.
  arXiv  Detail & Related papers  (2023-10-25T06:57:59Z)
• 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)
• Improving Open-Set Semi-Supervised Learning with Self-Supervision [13.944469874692459]
  Open-set semi-supervised learning (OSSL) embodies a practical scenario within semi-supervised learning. We propose an OSSL framework that facilitates learning from all unlabeled data through self-supervision. Our method yields state-of-the-art results on many of the evaluated benchmark problems.
  arXiv  Detail & Related papers  (2023-01-24T16:46:37Z)
• Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision? [51.00034621304361]
  We study decentralized learning with unlabeled data through the lens of self-supervised learning (SSL) We study the effectiveness of contrastive learning algorithms under decentralized learning settings.
  arXiv  Detail & Related papers  (2022-10-20T01:32:41Z)
• 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)
• OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning [110.40285771431687]
  Semi-supervised learning (SSL) is one of the dominant approaches to address the annotation bottleneck of supervised learning. Recent SSL methods can effectively leverage a large repository of unlabeled data to improve performance while relying on a small set of labeled data. This work introduces OpenLDN that utilizes a pairwise similarity loss to discover novel classes.
  arXiv  Detail & Related papers  (2022-07-05T18:51:05Z)
• Class-Aware Contrastive Semi-Supervised Learning [51.205844705156046]
  We propose a general method named Class-aware Contrastive Semi-Supervised Learning (CCSSL) to improve pseudo-label quality and enhance the model's robustness in the real-world setting. Our proposed CCSSL has significant performance improvements over the state-of-the-art SSL methods on the standard datasets CIFAR100 and STL10.
  arXiv  Detail & Related papers  (2022-03-04T12:18:23Z)

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.