We present HUME, a simple model-agnostic framework for inferring human labeling of a given dataset without any external supervision. The key insight behind our approach is that classes defined by many human labelings are linearly separable regardless of the representation space used to represent a dataset. HUME utilizes this insight to guide the search over all possible labelings of a dataset to discover an underlying human labeling. We show that the proposed optimization objective is strikingly well-correlated with the ground truth labeling of the dataset. In effect, we only train linear classifiers on top of pretrained representations that remain fixed during training, making our framework compatible with any large pretrained and self-supervised model. Despite its simplicity, HUME outperforms a supervised linear classifier on top of self-supervised representations on the STL-10 dataset by a large margin and achieves comparable performance on the CIFAR-10 dataset. Compared to the existing unsupervised baselines, HUME achieves state-of-the-art performance on four benchmark image classification datasets including the large-scale ImageNet-1000 dataset. Altogether, our work provides a fundamentally new view to tackle unsupervised learning by searching for consistent labelings between different representation spaces.

HUME是一个简单的模型无关框架，用于推断给定数据集的人类标签，无需任何外部监督。通过利用人类标签定义的类别在表示空间上是线性可分的洞察，HUME指导数据集的所有可能标签的搜索，以发现潜在的人类标签。尽管其简单性，HUME在多个基准图像分类数据集上优于监督线性分类器，包括大规模的ImageNet-1000数据集，为无监督学习提供了一种基本新的视角。

追求人类标注：无监督学习的新视角