Learning meaningful representations is at the heart of many tasks in the field of modern machine learning. Recently, a lot of methods were introduced that allow learning of image representations without supervision. These representations can then be used in downstream tasks like classification or object detection. The quality of these representations is close to supervised learning, while no labeled images are needed. This survey paper provides a comprehensive review of these methods in a unified notation, points out similarities and differences of these methods, and proposes a taxonomy which sets these methods in relation to each other. Furthermore, our survey summarizes the most-recent experimental results reported in the literature in form of a meta-study. Our survey is intended as a starting point for researchers and practitioners who want to dive into the field of representation learning.

翻译：学习有意义的表示是当今机器学习领域许多任务的核心。近期，大量无需监督即可学习图像表示的方法被提出。这些表示可用于下游任务，如分类或目标检测。其表示质量接近监督学习，且无需标注图像。本综述论文以统一符号体系全面回顾了这些方法，指出了方法间的异同，并提出了一种分类体系以建立这些方法间的关联。此外，我们的综述以元研究形式总结了文献中最新的实验结果。本综述旨在为希望深入表示学习领域的研究人员和实践者提供起点。