Unsupervised representation learning aims at finding methods that learn representations from data without annotation-based signals. Abstaining from annotations not only leads to economic benefits but may - and to some extent already does - result in advantages regarding the representation's structure, robustness, and generalizability to different tasks. In the long run, unsupervised methods are expected to surpass their supervised counterparts due to the reduction of human intervention and the inherently more general setup that does not bias the optimization towards an objective originating from specific annotation-based signals. While major advantages of unsupervised representation learning have been recently observed in natural language processing, supervised methods still dominate in vision domains for most tasks. In this dissertation, we contribute to the field of unsupervised (visual) representation learning from three perspectives: (i) Learning representations: We design unsupervised, backpropagation-free Convolutional Self-Organizing Neural Networks (CSNNs) that utilize self-organization- and Hebbian-based learning rules to learn convolutional kernels and masks to achieve deeper backpropagation-free models. (ii) Evaluating representations: We build upon the widely used (non-)linear evaluation protocol to define pretext- and target-objective-independent metrics for measuring and investigating the objective function mismatch between various unsupervised pretext tasks and target tasks. (iii) Transferring representations: We contribute CARLANE, the first 3-way sim-to-real domain adaptation benchmark for 2D lane detection, and a method based on prototypical self-supervised learning. Finally, we contribute a content-consistent unpaired image-to-image translation method that utilizes masks, global and local discriminators, and similarity sampling to mitigate content inconsistencies.

翻译：无监督表示学习旨在寻找无需基于标注信号即可从数据中学习表示的方法。摒弃标注不仅带来经济上的收益，还可能——并在一定程度上已经——在表示的结构、鲁棒性以及对不同任务的泛化能力方面带来优势。从长远来看，无监督方法有望超越其监督对应方法，因为其减少了人为干预，且其本质上更通用的设置不会使优化过程偏向于源自特定标注信号的目标。尽管无监督表示学习的主要优势近期在自然语言处理中已有所体现，但监督方法在视觉领域的多数任务中仍占主导地位。本论文从三个角度对无监督（视觉）表示学习领域做出贡献：(i) 学习表示：我们设计了无监督、无需反向传播的卷积自组织神经网络（CSNNs），利用自组织学习和赫布学习规则来学习卷积核与掩码，从而构建更深的无需反向传播模型。(ii) 评估表示：我们基于广泛使用的（非）线性评估协议，定义了与预任务和目标任务无关的度量标准，用于测量和研究各类无监督预任务与目标任务之间的目标函数不匹配问题。(iii) 迁移表示：我们贡献了CARLANE——首个用于二维车道检测的三路模拟到真实域适应基准数据集，以及一种基于原型自监督学习的方法。最后，我们提出了一种内容一致的成对无图像翻译方法，该方法利用掩码、全局与局部判别器以及相似性采样来缓解内容不一致问题。