Domain segmentation and adjustment for generalized zero-shot learning

Abstract

In the generalized zero-shot learning, synthesizing unseen data with generative models has been the most popular method to address the imbalance of training data between seen and unseen classes. However, this method requires that the unseen semantic information is available during the training stage, and training generative models is not trivial. Given that the generator of these models can only be trained with seen classes, we argue that synthesizing unseen data may not be an ideal approach for addressing the domain shift caused by the imbalance of the training data. In this paper, we propose to realize the generalized zero-shot recognition in different domains. Thus, unseen (seen) classes can avoid the effect of the seen (unseen) classes. In practice, we propose a threshold and probabilistic distribution joint method to segment the testing instances into seen, unseen and uncertain domains. Moreover, the uncertain domain is further adjusted to alleviate the domain shift. Extensive experiments on five benchmark datasets show that the proposed method exhibits competitive performance compared with that based on generative models.

References in corpus (13)

• Distilling the Knowledge in a Neural Network
• A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
• Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
• Learning Deep Representations of Fine-grained Visual Descriptions
• Semantic Autoencoder for Zero-Shot Learning
• Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
• Leveraging the Invariant Side of Generative Zero-Shot Learning
• Preserving Semantic Relations for Zero-Shot Learning
• An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild
• Latent Embeddings for Zero-shot Classification
• Deep CNN-based Multi-task Learning for Open-Set Recognition
• Learning Class Prototypes via Structure Alignment for Zero-Shot Recognition
• Visual Space Optimization for Zero-shot Learning

Cited by in corpus (1)

• A Review of Generalized Zero-Shot Learning Methods