Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning

• URL: http://arxiv.org/abs/2101.03292v1
• Date: Sat, 9 Jan 2021 05:21:27 GMT
• Title: Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning
• Authors: Zhi Chen, Zi Huang, Jingjing Li, Zheng Zhang
• Abstract summary: The goal of generalized zero-shot learning (GZSL) is to recognise both seen and unseen classes. Most GZSL methods typically learn to synthesise visual representations from semantic information on the unseen classes. We propose a novel framework that leverages dual variational autoencoders with a triplet loss to learn discriminative latent features.
• Score: 49.04790688256481
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compared to conventional zero-shot learning (ZSL) where recognising unseen classes is the primary or only aim, the goal of generalized zero-shot learning (GZSL) is to recognise both seen and unseen classes. Most GZSL methods typically learn to synthesise visual representations from semantic information on the unseen classes. However, these types of models are prone to overfitting the seen classes, resulting in distribution overlap between the generated features of the seen and unseen classes. The overlapping region is filled with uncertainty as the model struggles to determine whether a test case from within the overlap is seen or unseen. Further, these generative methods suffer in scenarios with sparse training samples. The models struggle to learn the distribution of high dimensional visual features and, therefore, fail to capture the most discriminative inter-class features. To address these issues, in this paper, we propose a novel framework that leverages dual variational autoencoders with a triplet loss to learn discriminative latent features and applies the entropy-based calibration to minimize the uncertainty in the overlapped area between the seen and unseen classes. Specifically, the dual generative model with the triplet loss synthesises inter-class discriminative latent features that can be mapped from either visual or semantic space. To calibrate the uncertainty for seen classes, we calculate the entropy over the softmax probability distribution from a general classifier. With this approach, recognising the seen samples within the seen classes is relatively straightforward, and there is less risk that a seen sample will be misclassified into an unseen class in the overlapped region. Extensive experiments on six benchmark datasets demonstrate that the proposed method outperforms state-of-the-art approaches.

Related papers

• Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection [64.4350027428928]
  We propose a novel uncertainty-guided class imbalance learning framework for imbalanced social event detection tasks. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones.
  arXiv  Detail & Related papers  (2023-10-30T03:32:04Z)
• Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning [59.44422468242455]
  We propose a novel method dubbed ShrinkMatch to learn uncertain samples. For each uncertain sample, it adaptively seeks a shrunk class space, which merely contains the original top-1 class. We then impose a consistency regularization between a pair of strongly and weakly augmented samples in the shrunk space to strive for discriminative representations.
  arXiv  Detail & Related papers  (2023-08-13T14:05:24Z)
• Generalization Bounds for Few-Shot Transfer Learning with Pretrained Classifiers [26.844410679685424]
  We study the ability of foundation models to learn representations for classification that are transferable to new, unseen classes. We show that the few-shot error of the learned feature map on new classes is small in case of class-feature-variability collapse.
  arXiv  Detail & Related papers  (2022-12-23T18:46:05Z)
• Resolving Semantic Confusions for Improved Zero-Shot Detection [6.72910827751713]
  We propose a generative model incorporating a triplet loss that acknowledges the degree of dissimilarity between classes. A cyclic-consistency loss is also enforced to ensure that generated visual samples of a class highly correspond to their own semantics.
  arXiv  Detail & Related papers  (2022-12-12T18:11:48Z)
• Self-Supervised Learning by Estimating Twin Class Distributions [26.7828253129684]
  We present TWIST, a novel self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We employ a siamese network terminated by a softmax operation to produce twin class distributions of two augmented images. Specifically, we minimize the entropy of the distribution for each sample to make the class prediction for each sample and maximize the entropy of the mean distribution to make the predictions of different samples diverse.
  arXiv  Detail & Related papers  (2021-10-14T14:39:39Z)
• Unsupervised Embedding Learning from Uncertainty Momentum Modeling [37.674449317054716]
  We propose a novel solution to explicitly model and explore the uncertainty of the given unlabeled learning samples. We leverage such uncertainty modeling momentum to the learning which is helpful to tackle the outliers.
  arXiv  Detail & Related papers  (2021-07-19T14:06:19Z)
• Understanding Classifier Mistakes with Generative Models [88.20470690631372]
  Deep neural networks are effective on supervised learning tasks, but have been shown to be brittle. In this paper, we leverage generative models to identify and characterize instances where classifiers fail to generalize. Our approach is agnostic to class labels from the training set which makes it applicable to models trained in a semi-supervised way.
  arXiv  Detail & Related papers  (2020-10-05T22:13:21Z)
• A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning [83.1490247844899]
  Generalized Zero-Shot Learning (GZSL) is a challenging topic that has promising prospects in many realistic scenarios. We propose a boundary based Out-of-Distribution (OOD) classifier which classifies the unseen and seen domains by only using seen samples for training. We extensively validate our approach on five popular benchmark datasets including AWA1, AWA2, CUB, FLO and SUN.
  arXiv  Detail & Related papers  (2020-08-09T11:27:19Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)

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.