Abstract 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.

Reconstructing images from magnitude measurements is an important and difficult problem arising in many research areas, such as X-ray crystallography, astronomical imaging and more. While optimization-based approaches often struggle with the non-convexity and non- linearity of the problem, learning-based approaches are able to produce reconstructions of high quality for data similar to a given training dataset. In this work, we analyze a class of methods based on conditional generative adversarial networks (CGAN). We show how the benefits of optimization-based and learning-based methods can be combined to improve reconstruction quality. Furthermore, we show that these combined methods are able to generalize to out-of-distribution data and analyze their robustness to measurement noise. In addition to that, we compare how the methods are impacted by missing measurements. Extensive ablation studies demonstrate that all components of our approach are essential and justify the choice of network architecture.

Abstract Model-based reinforcement learning algorithms try to learn an agent by training a model that simulates the environment. However, the size of such models tends to be quite large which could be a burden as well. In this paper, we address the question, how we could design a model with fewer parameters than previous model-based approaches while achieving the same performance in the 100 K-interactions regime. For this purpose, we create a world model that combines a vector quantized-variational autoencoder to encode observations and a convolutional long short-term memory to model the dynamics. This is connected to a model-free proximal policy optimization agent to train purely on simulated experience from this world model. Detailed experiments on the Atari environments show that it is possible to reach comparable performance to the SimPLe method with a significantly smaller world model. A series of ablation studies justify our design choices and give additional insights.

Alpha matting aims to estimate the translucency of an object in a given image. The resulting alpha matte describes pixel-wise to what amount foreground and background colors contribute to the color of the composite image. While most methods in literature focus on estimating the alpha matte, the process of estimating the foreground colors given the input image and its alpha matte is often neglected, although foreground estimation is an essential part of many image editing workflows. In this work, we propose a novel method for foreground estimation given the alpha matte. We demonstrate that our fast multi-level approach yields results that are comparable with the state-of-the-art while outperforming those methods in computational runtime and memory usage.

In this paper, we propose the application of conditional generative adversarial networks to solve various phase retrieval problems. We show that including knowledge of the measurement process at training time leads to an optimization at test time that is more robust to initialization than existing approaches involving generative models. In addition, conditioning the generator network on the measurements enables us to achieve much more detailed results. We empirically demonstrate that these advantages provide meaningful solutions to the Fourier and the compressive phase retrieval problem and that our method outperforms well-established projection-based methods as well as existing methods that are based on neural networks. Like other deep learning methods, our approach is robust to noise and can therefore be useful for real-world applications.