motivation:提出了两个关于zero-shot的问题:(1)在训练集上训练的从视觉空间到语义空间的匹配,直接应用在测试集上会出现project doman shift(领域偏移)的问题。(2)一般只用给定的语义信息(类描述)作为原型,会出现prototype sparsity problem,即一个原型无法很好的表示类的分布。 方法:针对这两个问题,作者提出了直推式...
由于嵌入空间是一个高维空间,所以很容易出现hubness problem。该问题是指:在高维空间中,一部分测试集的类别可能会成为很多数据点的K近邻(KNN),但其类别之间却没什么关系。当我们使用语义空间(semantic space)作为嵌入空间时,需要将视觉特征映射到语义空间中,这样会使得空间发生萎缩,点与点之间更加稠密,从而加重hubness...
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 ...
Improving zero-shot learning by mitigating the hubness problem. In: ICLR Workshop papers.Georgiana Dinu, Angeliki Lazaridou, and Marco Ba- roni. 2015. Improving zero-shot learning by miti- gating the hubness problem. In ICLR Workshop Pa- pers....
The training scheme of 3D ZS-DeconvNet integrates the spatially interleaved self-supervised learning scheme9 with the self-supervised inverse problem solver. In the training process, each noisy image stack was divided into odd slices and even slices, which were then used as input and targets, res...
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: ...
¿Cómo funciona el aprendizaje zero-shot? En ausencia de ejemplos etiquetados de las categorías para las que se entrena al modelo, los problemas de aprendizaje zero-shot utilizaninformación auxiliar: descripciones textuales, atributos, representaciones incrustadas u otra información semántica relev...
在很多机器学习任务中,模型在训练(training)时所采用的样本和模型在测试(testing)时所采用的样本分布(domain adaptation)不一致,导致了领域适应性问题(Problem of Domain Adaptation)。Domain Adaptation尝试去建立一个在training和Testing都适用的模型,用概率统计表示成如下形式:...
A study explains that zero-shot machine learning is used to construct recognition models for unseen target classes that have not labelled for training.
The experimental results show that zero-shot PS prompting consistently outperforms Zero-shot-CoT prompting across all datasets, is comparable to or exceeds Zero-shot-Program-of-Thought (PoT) prompting, and has comparable performance with 8-shot CoT prompting on the math reasoning problem. Key Take...