Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations

• URL: http://arxiv.org/abs/2003.12237v1
• Date: Fri, 27 Mar 2020 05:04:24 GMT
• Title: Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations
• Authors: Shuhao Cui, Shuhui Wang, Junbao Zhuo, Liang Li, Qingming Huang, Qi Tian
• Abstract summary: Batch Nuclear-norm Maximization (BNM) is proposed to boost the learning under label insufficient learning scenarios. BNM outperforms competitors and works well with existing well-known methods.
• Score: 154.51144248210338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The learning of the deep networks largely relies on the data with human-annotated labels. In some label insufficient situations, the performance degrades on the decision boundary with high data density. A common solution is to directly minimize the Shannon Entropy, but the side effect caused by entropy minimization, i.e., reduction of the prediction diversity, is mostly ignored. To address this issue, we reinvestigate the structure of classification output matrix of a randomly selected data batch. We find by theoretical analysis that the prediction discriminability and diversity could be separately measured by the Frobenius-norm and rank of the batch output matrix. Besides, the nuclear-norm is an upperbound of the Frobenius-norm, and a convex approximation of the matrix rank. Accordingly, to improve both discriminability and diversity, we propose Batch Nuclear-norm Maximization (BNM) on the output matrix. BNM could boost the learning under typical label insufficient learning scenarios, such as semi-supervised learning, domain adaptation and open domain recognition. On these tasks, extensive experimental results show that BNM outperforms competitors and works well with existing well-known methods. The code is available at https://github.com/cuishuhao/BNM.

Related papers

• Dirichlet-Based Prediction Calibration for Learning with Noisy Labels [40.78497779769083]
  Learning with noisy labels can significantly hinder the generalization performance of deep neural networks (DNNs) Existing approaches address this issue through loss correction or example selection methods. We propose the textitDirichlet-based Prediction (DPC) method as a solution.
  arXiv  Detail & Related papers  (2024-01-13T12:33:04Z)
• RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision [21.393509817509464]
  This paper proposes a novel semi-supervised anomaly detection method, which devises textitcontamination-resilient continuous supervisory signals Our approach significantly outperforms state-of-the-art competitors by 20%-30% in AUC-PR.
  arXiv  Detail & Related papers  (2023-07-25T04:04:49Z)
• Combating Mode Collapse in GANs via Manifold Entropy Estimation [70.06639443446545]
  Generative Adversarial Networks (GANs) have shown compelling results in various tasks and applications. We propose a novel training pipeline to address the mode collapse issue of GANs.
  arXiv  Detail & Related papers  (2022-08-25T12:33:31Z)
• Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation [154.2195491708548]
  We study the prediction discriminability and diversity by studying the structure of the classification output matrix of a randomly selected data batch. We propose Batch Nuclear-norm Maximization and Minimization, which performs nuclear-norm on the target output matrix to enhance the target prediction ability. Experiments show that our method could boost the adaptation accuracy and robustness under three typical domain adaptation scenarios.
  arXiv  Detail & Related papers  (2021-07-13T15:08:32Z)
• Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity [77.78624443410216]
  We propose improved FDAM algorithms for detecting heterogeneous chest data. A result of this paper is that the communication of the proposed algorithm is strongly independent of the number of machines and also independent of the accuracy level. Experiments have demonstrated the effectiveness of our FDAM algorithm on benchmark datasets and on medical chest Xray images from different organizations.
  arXiv  Detail & Related papers  (2021-02-09T04:05:19Z)
• A Unified Joint Maximum Mean Discrepancy for Domain Adaptation [73.44809425486767]
  This paper theoretically derives a unified form of JMMD that is easy to optimize. From the revealed unified JMMD, we illustrate that JMMD degrades the feature-label dependence that benefits to classification. We propose a novel MMD matrix to promote the dependence, and devise a novel label kernel that is robust to label distribution shift.
  arXiv  Detail & Related papers  (2021-01-25T09:46:14Z)
• Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation [99.92568326314667]
  We propose the amortized conditional normalized maximum likelihood (ACNML) method as a scalable general-purpose approach for uncertainty estimation. Our algorithm builds on the conditional normalized maximum likelihood (CNML) coding scheme, which has minimax optimal properties according to the minimum description length principle. We demonstrate that ACNML compares favorably to a number of prior techniques for uncertainty estimation in terms of calibration on out-of-distribution inputs.
  arXiv  Detail & Related papers  (2020-11-05T08:04:34Z)
• Bayesian Semi-supervised Multi-category Classification under Nonparanormality [2.307581190124002]
  Semi-supervised learning is a model training method that uses both labeled and unlabeled data. This paper proposes a fully Bayes semi-supervised learning algorithm that can be applied to any multi-category classification problem.
  arXiv  Detail & Related papers  (2020-01-11T21:31:25Z)

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.