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.

无监督表示学习、卷积自组织神经网络、评估协议、表示传输、图像到图像转换

走向无监督表示学习：学习、评估和传递视觉表示

Towards Unsupervised Representation Learning: Learning, Evaluating and  Transferring Visual Representations

This work combines Convolutional Neural Networks (CNNs), clustering via
Self-Organizing Maps (SOMs) and Hebbian Learning to propose the building blocks
of Convolutional Self-Organizing Neural Networks (CSNNs), which learn
representations in an unsupervised and Backpropagation-free manner. Our
approach replaces the learning of traditional convolutional layers from CNNs
with the competitive learning procedure of SOMs and simultaneously learns local
masks between those layers with separate Hebbian-like learning rules to
overcome the problem of disentangling factors of variation when filters are
learned through clustering. We investigate the learned representation by
designing two simple models with our building blocks, achieving comparable
performance to many methods which use Backpropagation, while we reach
comparable performance on Cifar10 and give baseline performances on Cifar100,
Tiny ImageNet and a small subset of ImageNet for Backpropagation-free methods.

本文提出了一种基于卷积神经网络、自组织映射聚类和赫布学习的卷积自组织神经网络（CSNNs）的建模方法，可以在无需反向传播的情况下完成学习，利用所提出的建模方法可以用于图像分类领域并取得与 Backpropagation 方法相当的性能。