图3 domain shift示意图,图中的prototype表示类别在语义空间中的位置[2] 枢纽点问题(Hubness problem) 这其实是高维空间中固有的问题:在高维空间中,某些点会成为大多数点的最近邻点。这听上去有些反直观,细节方面可以参考[3]。由于ZSL在计算最终的正确率时,使用的是K-NN,所以会受到hubness problem的影响,并且[...
领域漂移问题(domain shift problem) 该问题的正式定义首先由[2]提出。简单来说,就是同一种属性,在不同的类别中,视觉特征的表现可能很大。如图3所示,斑马和猪都有尾巴,因此在它的类别语义表示中,“有尾巴”这一项都是非0值,但是两者尾巴的视觉特征却相差很远。如果斑马是训练集,而猪是测试集,那么利用斑马训练出...
虽然如前文所说,零样本学习仍处于快速发展的阶段,但零样本学习由于其自身方法中存在的问题,这些问题使得零样本学习的研究遇到很大的障碍。这三个障碍分别是广义(泛化)零样本学习(Generalized zero-shot learning)、枢纽化问题(Hubness)、映射域偏移问题(The projection domain shift problem)。下面简单介绍一下这几个问...
In recent years, self-supervised learning has had significant success in applications involving computer vision and natural language processing. The type of pretext task is important to this boost in performance. One common pretext task is the measure of similarity and dissimilarity between pairs of ...
Chinese character recognition has attracted much research interest due to its wide applications. Although it has been studied for many years, some issues in this field have not been completely resolved yet, extit{e.g.} the zero-shot problem. Previous character-based and radical-based methods hav...
说到ood，要从传统ml中的supervised learning讲起。传统意义上的machine learning problem，是指对于有限的...
Improving zero-shot learning by mitigating the hubness problem. In ICLR 2015, Workshop Track, 2015.Georgiana Dinu, Angeliki Lazaridou, and Marco Ba- roni. 2014. Improving zero-shot learning by miti- gating the hubness problem. In Proceedings of ICLR Workshop, San Diego, California....
Zero-shot learning transfers knowledge from seen classes to novel unseen classes to reduce human labor of labelling data for building new classifiers. Much effort on zero-shot learning however has focused on the standard multi-class setting, the more challenging multi-label zero-shot problem has re...
零样本学习是一种机器学习的问题设置,其中模型可以对从未在训练过程中见过的类别的样本进行分类,使用一些形式的辅助信息来关联已见和未见的类别。例如,一个模型可以根据动物的文本描述来识别动物,即使它从未见过那些动物的图像。 实现零样本学习有不同的方法,取决于辅助信息的类型和学习方法。以下是一些例子: ...
One-shot learning is an object categorization problem in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or onl...