论文标题
在类不平衡数据集上用于广义零射击学习的高效高斯流程模型
Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning
论文作者
论文摘要
零拍学习（ZSL）模型旨在对训练过程中未看到的对象类进行分类。但是，尽管在多个ZSL数据集中存在，但很少讨论类失衡的问题。在本文中，我们提出了一个神经网络模型，该模型了解潜在特征嵌入和一个高斯过程（GP）回归模型，该模型可以预测看不见类别的潜在特征原型。然后为ZSL和广义ZSL任务构建校准的分类器。我们的神经网络模型通过简单的培训策略有效地进行了培训，该策略减轻了类不平衡培训数据的影响。该模型的平均培训时间为5分钟，可以在不平衡的ZSL基准数据集（如AWA2，AWA1和APY）上实现最先进的（SOTA）性能，同时在阳光和幼崽数据集中的性能相对较好。
Zero-Shot Learning (ZSL) models aim to classify object classes that are not seen during the training process. However, the problem of class imbalance is rarely discussed, despite its presence in several ZSL datasets. In this paper, we propose a Neural Network model that learns a latent feature embedding and a Gaussian Process (GP) regression model that predicts latent feature prototypes of unseen classes. A calibrated classifier is then constructed for ZSL and Generalized ZSL tasks. Our Neural Network model is trained efficiently with a simple training strategy that mitigates the impact of class-imbalanced training data. The model has an average training time of 5 minutes and can achieve state-of-the-art (SOTA) performance on imbalanced ZSL benchmark datasets like AWA2, AWA1 and APY, while having relatively good performance on the SUN and CUB datasets.
