SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning

• URL: http://arxiv.org/abs/2311.10572v1
• Date: Fri, 17 Nov 2023 15:14:40 GMT
• Title: SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning
• Authors: Yue Fan, Anna Kukleva, Dengxin Dai, Bernt Schiele
• Abstract summary: In this paper, we study the challenging and realistic open-set SSL setting. The goal is to both correctly classify inliers and to detect outliers. We find that inlier classification performance can be largely improved by incorporating high-confidence pseudo-labeled data.
• Score: 106.46648817126984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semi-supervised learning (SSL) methods effectively leverage unlabeled data to improve model generalization. However, SSL models often underperform in open-set scenarios, where unlabeled data contain outliers from novel categories that do not appear in the labeled set. In this paper, we study the challenging and realistic open-set SSL setting, where the goal is to both correctly classify inliers and to detect outliers. Intuitively, the inlier classifier should be trained on inlier data only. However, we find that inlier classification performance can be largely improved by incorporating high-confidence pseudo-labeled data, regardless of whether they are inliers or outliers. Also, we propose to utilize non-linear transformations to separate the features used for inlier classification and outlier detection in the multi-task learning framework, preventing adverse effects between them. Additionally, we introduce pseudo-negative mining, which further boosts outlier detection performance. The three ingredients lead to what we call Simple but Strong Baseline (SSB) for open-set SSL. In experiments, SSB greatly improves both inlier classification and outlier detection performance, outperforming existing methods by a large margin. Our code will be released at https://github.com/YUE-FAN/SSB.

Related papers

• IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization [36.102831230805755]
  In many real-world applications, unlabeled data will inevitably contain unseen-class outliers not belonging to any of the labeled classes. We introduce a novel open-set SSL framework, IOMatch, which can jointly utilize inliers and outliers, even when it is difficult to distinguish exactly between them.
  arXiv  Detail & Related papers  (2023-08-25T04:14:02Z)
• Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning [69.81438976273866]
  Open-set semi-supervised learning (Open-set SSL) considers a more practical scenario, where unlabeled data and test data contain new categories (outliers) not observed in labeled data (inliers) We introduce evidential deep learning (EDL) as an outlier detector to quantify different types of uncertainty, and design different uncertainty metrics for self-training and inference. We propose a novel adaptive negative optimization strategy, making EDL more tailored to the unlabeled dataset containing both inliers and outliers.
  arXiv  Detail & Related papers  (2023-03-21T09:07:15Z)
• RbA: Segmenting Unknown Regions Rejected by All [1.3381749415517021]
  We show that the object queries in mask classification tend to behave like one vs all classifiers. We propose a novel outlier scoring function called RbA by defining the event of being an outlier as being rejected by all known classes. Our experiments show that mask classification improves the performance of the existing outlier detection methods, and the best results are achieved with the proposed RbA.
  arXiv  Detail & Related papers  (2022-11-25T18:50:04Z)
• 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)
• L2B: Learning to Bootstrap Robust Models for Combating Label Noise [52.02335367411447]
  This paper introduces a simple and effective method, named Learning to Bootstrap (L2B) It enables models to bootstrap themselves using their own predictions without being adversely affected by erroneous pseudo-labels. It achieves this by dynamically adjusting the importance weight between real observed and generated labels, as well as between different samples through meta-learning.
  arXiv  Detail & Related papers  (2022-02-09T05:57:08Z)
• Unsupervised Outlier Detection using Memory and Contrastive Learning [53.77693158251706]
  We think outlier detection can be done in the feature space by measuring the feature distance between outliers and inliers. We propose a framework, MCOD, using a memory module and a contrastive learning module. Our proposed MCOD achieves a considerable performance and outperforms nine state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-07-27T07:35:42Z)
• Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning [26.069534478556527]
  Semi-Supervised Learning (SSL) has shown its strong ability in utilizing unlabeled data when labeled data is scarce. Most SSL algorithms work under the assumption that the class distributions are balanced in both training and test sets. In this work, we consider the problem of SSL on class-imbalanced data, which better reflects real-world situations.
  arXiv  Detail & Related papers  (2021-06-01T03:58:18Z)
• OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers [71.08167292329028]
  We propose a novel Open-set Semi-Supervised Learning (OSSL) approach called OpenMatch. OpenMatch unifies FixMatch with novelty detection based on one-vs-all (OVA) classifiers. It achieves state-of-the-art performance on three datasets, and even outperforms a fully supervised model in detecting outliers unseen in unlabeled data on CIFAR10.
  arXiv  Detail & Related papers  (2021-05-28T23:57:15Z)

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.