Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The PASCAL Visual Object Classes Challenge

Real and Complex Analysis

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.

/pdf/ilastik-interactive-machine-learning-for-bio-image-analysis-57nb1zt342.pdf

ilastik: interactive machine learning for (bio)image analysis.

Segmentation is the process of partitioning digital images into meaningful regions. The analysis of biological high content images often requires segmentation as a first step. We propose ilastik as an easy-to-use tool which allows the user without expertise in image processing to perform segmentation and classification in a unified way. ilastik learns from labels provided by the user through a convenient mouse interface. Based on these labels, ilastik infers a problem specific segmentation. A random forest classifier is used in the learning step, in which each pixel's neighborhood is characterized by a set of generic (nonlinear) features. ilastik supports up to three spatial plus one spectral dimension and makes use of all dimensions in the feature calculation. ilastik provides realtime feedback that enables the user to interactively refine the segmentation result and hence further fine-tune the classifier. An uncertainty measure guides the user to ambiguous regions in the images. Real time performance is achieved by multi-threading which fully exploits the capabilities of modern multi-core machines. Once a classifier has been trained on a set of representative images, it can be exported and used to automatically process a very large number of images (e.g. using the CellProfiler pipeline). ilastik is an open source project and released under the BSD license at www.ilastik.org.

Ilastik: Interactive learning and segmentation toolkit

In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning input into useful features. All parameters of the cINN are jointly optimized with a stable, maximum likelihood-based training procedure. By construction, the cINN does not experience mode collapse and generates diverse samples, in contrast to e.g. cGANs. At the same time our model produces sharp images since no reconstruction loss is required, in contrast to e.g. VAEs. We demonstrate these properties for the tasks of MNIST digit generation and image colorization. Furthermore, we take advantage of our bi-directional cINN architecture to explore and manipulate emergent properties of the latent space, such as changing the image style in an intuitive way.

Guided Image Generation with Conditional Invertible Neural Networks

In many tasks, in particular in natural science, the goal is to determine hidden system parameters from a set of measurements. Often, the forward process from parameter- to measurement-space is a well-defined function, whereas the inverse problem is ambiguous: one measurement may map to multiple different sets of parameters. In this setting, the posterior parameter distribution, conditioned on an input measurement, has to be determined. We argue that a particular class of neural networks is well suited for this task -- so-called Invertible Neural Networks (INNs). Although INNs are not new, they have, so far, received little attention in literature. While classical neural networks attempt to solve the ambiguous inverse problem directly, INNs are able to learn it jointly with the well-defined forward process, using additional latent output variables to capture the information otherwise lost. Given a specific measurement and sampled latent variables, the inverse pass of the INN provides a full distribution over parameter space. We verify experimentally, on artificial data and real-world problems from astrophysics and medicine, that INNs are a powerful analysis tool to find multi-modalities in parameter space, to uncover parameter correlations, and to identify unrecoverable parameters.

/pdf/analyzing-inverse-problems-with-invertible-neural-networks-4wvn1qqdbo.pdf

Analyzing Inverse Problems with Invertible Neural Networks

We describe three modifications to the structure tensor approach to low-level feature extraction. We first show that the structure tensor must be represented at a higher resolution than the original image. Second, we propose a non-linear filter for structure tensor computation that avoids undesirable blurring. Third, we introduce a method to simultaneously extract edge and junction information. Examples demonstrate significant improvements in the quality of the extracted features.

Ullrich Köthe

Papers

Ilastik: Interactive learning and segmentation toolkit

Guided Image Generation with Conditional Invertible Neural Networks

Analyzing Inverse Problems with Invertible Neural Networks

Analyzing Inverse Problems with Invertible Neural Networks

Edge and Junction Detection with an Improved Structure Tensor