Neural Network to Change Background in Any Image

Massachusetts Institute of Technology researchers had created convinient image editor, based on AI
23 August 2018   1496

Researchers from the Massachusetts Institute of Technology (MIT) presented an algorithm for a convolutional neural network, which automatically transfers objects from one image to another. In this case, the user does not need to select parts of the image or define their boundaries. This is reported by The Next Web.

The editor, called Semantic Soft Segmentation (SSS), divides objects and background into different segments. The system analyzes the color, transparency and texture of the edges of objects. It takes into account the semantic proximity of the pixels: they can belong to two objects simultaneously. As a result, on a new background, the objects look clear and without torn edges. The algorithm processes one image on average in 4 minutes.

In August 2018, the author of the blog, AI Weirdness told about the generative-controversial neural network AttnGAN, which draws images by text description. The problem with the algorithm is that it requires too precisely defined picture parameters and sometimes can not determine the boundaries of objects.

TensorFlow 2.0 to be Released

New major release of the machine learning platform brought a lot of updates and changes, some stuff even got cut
01 October 2019   241

A significant release of the TensorFlow 2.0 machine learning platform is presented, which provides ready-made implementations of various deep machine learning algorithms, a simple programming interface for building models in Python, and a low-level interface for C ++ that allows you to control the construction and execution of computational graphs. The system code is written in C ++ and Python and is distributed under the Apache license.

The platform was originally developed by the Google Brain team and is used in Google services for speech recognition, facial recognition in photographs, determining the similarity of images, filtering spam in Gmail, selecting news in Google News and organizing the translation taking into account the meaning. Distributed machine learning systems can be created on standard equipment, thanks to the built-in support in TensorFlow for spreading computing to multiple CPUs or GPUs.

TensorFlow provides a library of off-the-shelf numerical computation algorithms implemented through data flow graphs. The nodes in such graphs implement mathematical operations or entry / exit points, while the edges of the graph represent multidimensional data arrays (tensors) that flow between the nodes. The nodes can be assigned to computing devices and run asynchronously, simultaneously processing all the suitable tensors at the same time, which allows you to organize the simultaneous operation of nodes in the neural network by analogy with the simultaneous activation of neurons in the brain.

Get more info about the update at official website.