Zero-shot Active Learning Using Self Supervised Learning
Abhishek Sinha Shreya Singh
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.