Computer Vision News - March 2019

Let’s assume our model is linear regression, and we are trying to fit a line to a given dataset. Look at the difference between the line on the left (fitted to the dataset including the outliers) and the data on the right (fitted after the outliers were removed). PyOD provides you with a wide variety of outlier detection algorithms, including established outlier ensembles and more modern neural-network-based approaches. All available through a single, well documented, API made with both industry users and researchers in mind. PyOD was implemented with emphasis on unit testing, continuous integration, code coverage, maintainability checks, interactive examples and parallelization. Here is the link to download PyOD (it is compatible with both Python 2 and 3) PyOD offers a number of advantages over previously available libraries: 1. PyOD includes over 20 algorithms, covering both classic techniques such as Local Outlier Factor and the latest neural network architectures like autoencoders or adversarial models. 2. Implements a number of methods for merging / combining the results of numerous outlier detectors. 3. Uses a unified API, includes detailed documentation with interactive examples of every method and algorithm for clarity and ease of use. 4. All functions and methods include code testing and continuous integration, parallel processing and just-in-time (JIT) compilation. 5. Can be run on both Python 2 and Python 3, and on all the major operating systems (Linux, Windows, and MacOS). 6. PyOD includes the following popular detection algorithms: 19 Focus on Computer Vision News Python open source toolbox for Outlier Detection