Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification
- URL: http://arxiv.org/abs/2407.03596v1
- Date: Thu, 4 Jul 2024 03:04:56 GMT
- Title: Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification
- Authors: Xuerong Zhang, Li Huang, Jing Lv, Ming Yang,
- Abstract summary: Pseudo-labeling-based semi-supervised approaches suffer from two problems in image classification.
We develop a self adaptive threshold pseudo-labeling strategy, which thresholds for each class can be dynamically adjusted to increase the number of reliable samples.
In order to effectively utilise unlabeled data with confidence below the thresholds, we propose an unreliable sample contrastive loss.
- Score: 6.920336485308536
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. However, pseudo-labeling-based semi-supervised approaches suffer from two problems in image classification: (1) Existing methods might fail to adopt suitable thresholds since they either use a pre-defined/fixed threshold or an ad-hoc threshold adjusting scheme, resulting in inferior performance and slow convergence. (2) Discarding unlabeled data with confidence below the thresholds results in the loss of discriminating information. To solve these issues, we develop an effective method to make sufficient use of unlabeled data. Specifically, we design a self adaptive threshold pseudo-labeling strategy, which thresholds for each class can be dynamically adjusted to increase the number of reliable samples. Meanwhile, in order to effectively utilise unlabeled data with confidence below the thresholds, we propose an unreliable sample contrastive loss to mine the discriminative information in low-confidence samples by learning the similarities and differences between sample features. We evaluate our method on several classification benchmarks under partially labeled settings and demonstrate its superiority over the other approaches.
Related papers
- AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning [5.0823084858349485]
We present a novel SSL algorithm named AllMatch, which achieves improved pseudo-label accuracy and a 100% utilization ratio for the unlabeled data.
The results demonstrate that AllMatch consistently outperforms existing state-of-the-art methods.
arXiv Detail & Related papers (2024-06-22T06:59:52Z) - Binary Classification with Confidence Difference [100.08818204756093]
This paper delves into a novel weakly supervised binary classification problem called confidence-difference (ConfDiff) classification.
We propose a risk-consistent approach to tackle this problem and show that the estimation error bound the optimal convergence rate.
We also introduce a risk correction approach to mitigate overfitting problems, whose consistency and convergence rate are also proven.
arXiv Detail & Related papers (2023-10-09T11:44:50Z) - Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and
Uncurated Unlabeled Data [70.25049762295193]
We introduce a novel conditional image generation framework that accepts noisy-labeled and uncurated data during training.
We propose soft curriculum learning, which assigns instance-wise weights for adversarial training while assigning new labels for unlabeled data.
Our experiments show that our approach outperforms existing semi-supervised and label-noise robust methods in terms of both quantitative and qualitative performance.
arXiv Detail & Related papers (2023-07-17T08:31:59Z) - Guiding Pseudo-labels with Uncertainty Estimation for Test-Time
Adaptation [27.233704767025174]
Test-Time Adaptation (TTA) is a specific case of Unsupervised Domain Adaptation (UDA) where a model is adapted to a target domain without access to source data.
We propose a novel approach for the TTA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels.
arXiv Detail & Related papers (2023-03-07T10:04:55Z) - SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised
Learning [101.86916775218403]
This paper revisits the popular pseudo-labeling methods via a unified sample weighting formulation.
We propose SoftMatch to overcome the trade-off by maintaining both high quantity and high quality of pseudo-labels during training.
In experiments, SoftMatch shows substantial improvements across a wide variety of benchmarks, including image, text, and imbalanced classification.
arXiv Detail & Related papers (2023-01-26T03:53:25Z) - Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for
Semi-Supervised Text Recognition [21.583569162994277]
One of the most popular SSL approaches is pseudo-labeling (PL)
PL methods are severely degraded by noise and are prone to over-fitting to noisy labels.
We propose a pseudo-label generation and an uncertainty-based data selection framework for text recognition.
arXiv Detail & Related papers (2022-08-31T02:21:02Z) - 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) - Semi-supervised Long-tailed Recognition using Alternate Sampling [95.93760490301395]
Main challenges in long-tailed recognition come from the imbalanced data distribution and sample scarcity in its tail classes.
We propose a new recognition setting, namely semi-supervised long-tailed recognition.
We demonstrate significant accuracy improvements over other competitive methods on two datasets.
arXiv Detail & Related papers (2021-05-01T00:43:38Z) - Exploiting Sample Uncertainty for Domain Adaptive Person
Re-Identification [137.9939571408506]
We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels.
Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
arXiv Detail & Related papers (2020-12-16T04:09:04Z)
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.