CryoETGAN: Cryo-Electron Tomography Image Synthesis via Unpaired Image Translation

Cryo-electron tomography (Cryo-ET) has been regarded as a revolution in structural biology and can reveal molecular sociology. Its unprecedented quality enables it to visualize cellular organelles and macromolecular complexes at nanometer resolution with native conformations. Motivated by developments in nanotechnology and machine learning, establishing machine learning approaches such as classification, detection and averaging for Cryo-ET image analysis has inspired broad interest. Yet, deep learning-based methods for biomedical imaging typically require large labeled datasets for good results, which can be a great challenge due to the expense of obtaining and labeling training data. To deal with this problem, we propose a generative model to simulate Cryo-ET images efficiently and reliably: CryoETGAN. This cycle-consistent and Wasserstein generative adversarial network (GAN) is able to generate images with an appearance similar to the original experimental data. Quantitative and visual grading results on generated images are provided to show that the results of our proposed method achieve better performance compared to the previous state-of-the-art simulation methods. Moreover, CryoETGAN is stable to train and capable of generating plausibly diverse image samples.

Computational Aesthetics and Image Enhancements using Deep Neural Networks

Bin Jin

Imaging devices have become ubiquitous in modern life, and many of us capture an increasing number of images every day. When we choose to share or store some of these images, our primary selection criterion is to choose the most visually pleasing ones. Yet, quantifying visual pleasantness is a challenge, as image aesthetics not only correlate with low-level image quality, such as contrast, but also high-level visual processes, like composition and context. For most users, a considerable amount of manual effort and/or professional knowledge is required to get aesthetically pleasing images. Developing automatic solutions thus benefits a large community. This thesis proposes several computational approaches to help users obtain the desired images. The first technique aims at automatically measuring the aesthetics quality, which benefits the users in selecting and ranking images. We form the aesthetics prediction problem as a regression task and train a deep neural network on a large image aesthetics dataset. The unbalanced distribution of aesthetics scores in the training set can result in bias of the trained model towards certain aesthetics levels. Therefore, we propose to add sample weights during training to overcome such bias. Moreover, we build a loss function on the histograms of user labels, thus enabling the network to predict not only the average aesthetics quality but also the difficulty of such predictions. Extensive experiments demonstrate that our model outperforms the previous state-of-the-art by a notable margin. Additionally, we propose an image cropping technique that automatically outputs aesthetically pleasing crops. Given an input image and a certain template, we first extract a sufficient amount of candidate crops. These crops are later ranked according to the scores predicted by the pre-trained aesthetics network, after which the best crop is output to the users. We conduct psychophysical experiments to validate the performance. We further present a keyword-based image color re-rendering algorithm. For this task, the colors in the input image are modified to be visually more appealing according to the keyword specified by users. Our algorithm applies local color re-rendering operations to achieve this goal. A novel weakly-supervised semantic segmentation algorithm is developed to locate the keyword-related regions where the color re-rendering operations are applied. The color re-rendering process benefits from the segmentation network in two aspects. Firstly, we achieve more accurate correlation measurements between keywords and color characteristics, contributing to better re-render rendering results of the colors. Secondly, the artifacts caused by the color re-rendering operations are significantly reduced. To avoid the need of keywords when enhancing image aesthetics, we explore generative adversarial networks (GANs) for automatic image enhancement. GANs are known for directly learning the transformations between images from the training data. To learn the image enhancement operations, we train the GANs on an aesthetics dataset with three different losses combined. The first two are standard generative losses that enforce the generated images to be natural and content-wise similar to the input images. We propose a third aesthetics loss that aims at improving the aesthetics quality of the generated images. Overall, the three losses together direct the GANs to apply appropriate image enhancement operations.

EPFL2018

Deep Generative Models and Applications

Tatjana Chavdarova

Over the past few years, there have been fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. The amount of annotated data drastically increased and supervised deep discriminative models exceeded human-level performances in certain object detection tasks. The increasing availability in quantity and complexity of unlabelled data also opens up exciting possibilities for the development of unsupervised learning methods. Among the family of unsupervised methods, deep generative models find numerous applications. Moreover, as real-world applications include high dimensional data, the ability of generative models to automatically learn semantically meaningful subspaces makes their advancement an essential step toward developing more efficient algorithms. Generative Adversarial Networks (GANs) are a family of unsupervised generative algorithms that have demonstrated impressive performance for data synthesis and are now used in a wide range of computer vision tasks. Despite this success, they gained a reputation for being difficult to train, which results in a time-consuming and human-involved development process to use them. In the first part of this thesis, we focus on improving the stability and the performances of GANs. Foremost, we consider an alternative training process to the standard one, named SGAN, in which several adversarial âlocalâ pairs of networks are trained independently so that a âglobalâ supervising pair of networks can be trained against them. Experimental results on both toy and real-world problems demonstrate that this approach outperforms standard training in terms of better mitigating mode collapse, stability while converging and that it surprisingly, increases the convergence speed as well. To further reduce the computational footprint while maintaining the stability and performance advantages of SGAN, we focus on training a single pair of adversarial networks using variance reduced gradient. More precisely, we study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can prevent the convergence of standard game optimization methods, while the batch version converges. We address this issue with two stochastic variance-reduced gradient and extragradient optimization algorithms for GANs, named SVRG-GAN and SVRE, respectively. We observe empirically that SVRE performs similarly to a batch method on the MNIST dataset, while being computationally cheaper, and that SVRE yields more stable GAN training on standard datasets. In the second part of the thesis we present our work on people detection. People detection methods are highly sensitive to occlusions between pedestrians, and using joint visual information from multiple synchronized cameras gives the opportunity to improve detection performance. We address the problem of multi-view people occupancy map estimation using an endâtoâend deep learning algorithm called DeepMCD that jointly utilizes the correlated streams of visual information. DeepMCD empirically outperformed the classical approaches by a large margin. Finally, we present a new large-scale and high-resolution dataset, named WILDTRACK. We provide an accurate joint calibration, as well as a series of benchmark results using baseline algorithms published over the recent months for multi-view detection with deep neural networks, and trajectory estimation using a non-Markovian model.

EPFL2020

Deep Generative Models and Applications

Tatjana Chavdarova

EPFL2020

Computational Aesthetics and Image Enhancements using Deep Neural Networks

Bin Jin

EPFL2018