Deep learning algorithms are often said to be data hungry. The performance of such algorithms generally improve as more and more annotated data is fed into the model. While collecting unlabelled data is easier (as they can be scraped easily from the internet), annotating them is a tedious and expensive task. Given a fixed budget available for data annotation, Active Learning helps selecting the best subset of data for annotation, such that the deep learning model when trained over that subset will have maximum generalization performance under this budget. In this work, we aim to propose a new Active Learning approach which is model agnostic as well as one doesn't require an iterative process. We aim to leverage self-supervised learnt features for the task of Active Learning. The benefit of self-supervised learning, is that one can get useful feature representation of the input data, without having any annotation.

翻译：深度学习算法常被描述为数据饥渴型。此类算法的性能通常随着向模型输入更多带标注数据而提升。虽然收集无标注数据相对容易（因为可以从互联网上轻松抓取），但对其进行标注却是一项繁琐且昂贵的任务。在给定数据标注固定预算的情况下，主动学习有助于选择最佳数据子集进行标注，使深度学习模型在该子集上训练后，在此预算下获得最大的泛化性能。本研究旨在提出一种新的主动学习方法，该方法既与模型无关，又无需迭代过程。我们计划利用自监督学习提取的特征来完成主动学习任务。自监督学习的优势在于，无需任何标注即可获得输入数据的有用特征表示。