论文标题
进化通用的零拍学习
Evolutionary Generalized Zero-Shot Learning
论文作者
论文摘要
基于属性的零拍学习（ZSL）已彻底改变了模型识别训练期间看不见的新课程的能力。但是，随着大型模型的发展，期望的增长已经上升。除了仅实现零拍的概括外，对通用模型的需求不断增长，可以使用未标记的数据在专家领域不断发展。为了解决这个问题，我们介绍了这一挑战的缩放实例化：进化的零射击学习（EGZSL）。此设置允许低绩效的零拍模型适应测试数据流并在线发展。我们阐述了这项特殊任务的三个挑战，即\ ie，灾难性的遗忘，初始预测偏见和进化数据类别的偏见。此外，我们为每个挑战提出了有针对性的解决方案，从而产生了一种能够从给定初始IGZSL模型中连续进化的通用方法。在三个流行的GZSL基准数据集上进行的实验表明，我们的模型可以在测试数据流中学习，而其他基线失败。代码可在\ url {https://github.com/cdb342/egzsl}中获得。
Attribute-based Zero-Shot Learning (ZSL) has revolutionized the ability of models to recognize new classes not seen during training. However, with the advancement of large-scale models, the expectations have risen. Beyond merely achieving zero-shot generalization, there is a growing demand for universal models that can continually evolve in expert domains using unlabeled data. To address this, we introduce a scaled-down instantiation of this challenge: Evolutionary Generalized Zero-Shot Learning (EGZSL). This setting allows a low-performing zero-shot model to adapt to the test data stream and evolve online. We elaborate on three challenges of this special task, \ie, catastrophic forgetting, initial prediction bias, and evolutionary data class bias. Moreover, we propose targeted solutions for each challenge, resulting in a generic method capable of continuous evolution from a given initial IGZSL model. Experiments on three popular GZSL benchmark datasets demonstrate that our model can learn from the test data stream while other baselines fail. Codes are available at \url{https://github.com/cdb342/EGZSL}.
