规模精度：按需领域特定数据集
Precision at Scale: Domain-Specific Datasets On-Demand
AI总结：
提出PaS方法，利用基础模型和生成模型按需自动创建领域特定数据集，在多个领域上优于ImageNet预训练，规模更小，性能提升显著。
摘要：
在自监督学习（SSL）领域，传统观点倾向于使用大规模、通用领域数据集来预训练稳健的主干网络。在本文中，我们通过探索是否有可能弥合通用领域数据集与（传统上较小的）领域特定数据集之间的规模差距，以减少当前性能差距，从而挑战这一观点。更具体地说，我们提出了“规模精度”（PaS），一种用于按需自主创建领域特定数据集的新方法。PaS流水线的模块化设计使得能够利用最先进的基础模型和生成模型，以最少的人工干预，创建属于任何领域的任意大小的图像集合。在两个复杂领域的广泛分析证明了PaS数据集在多样性、规模以及训练视觉变换器和卷积神经网络的有效性方面优于现有的传统领域特定数据集。最值得注意的是，我们证明了自动生成的领域特定数据集比大规模监督数据集（如ImageNet-1k和ImageNet-21k）能带来更好的预训练效果。具体来说，在PaS流水线构建的领域特定数据集上训练的模型，在所有考虑的领域和分类任务中，比ImageNet-1k预训练主干网络至少高出12%，并且在食品领域性能优于监督的ImageNet-21k预训练，同时规模小12倍。代码仓库：此https URL
英文摘要：
In the realm of self-supervised learning (SSL), conventional wisdom has gravitated towards the utility of massive, general domain datasets for pretraining robust backbones. In this paper, we challenge this idea by exploring if it is possible to bridge the scale between general-domain datasets and (traditionally smaller) domain-specific datasets to reduce the current performance gap. More specifically, we propose Precision at Scale (PaS), a novel method for the autonomous creation of domain-specific datasets on-demand. The modularity of the PaS pipeline enables leveraging state-of-the-art foundational and generative models to create a collection of images of any given size belonging to any given domain with minimal human intervention. Extensive analysis in two complex domains, proves the superiority of PaS datasets over existing traditional domain-specific datasets in terms of diversity, scale, and effectiveness in training visual transformers and convolutional neural networks. Most notably, we prove that automatically generated domain-specific datasets lead to better pretraining than large-scale supervised datasets such as ImageNet-1k and ImageNet-21k. Concretely, models trained on domain-specific datasets constructed by PaS pipeline, beat ImageNet-1k pretrained backbones by at least 12% in all the considered domains and classification tasks and lead to better food domain performance than supervised ImageNet-21k pretrain while being 12 times smaller. Code repository: https://github.com/jesusmolrdv/Precision-at-Scale/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
