AttnGAN Neural Network to Draw Strange Pictures

The neural network is good at drawing birds by the text description, but bad everything else
20 August 2018   839

The author of the AI Weirdness blog Janelle Shane had discovered the generative-controversial neural network called AttnGAN, which is trained to draw images on the text description. The problem is that it requires too accurately defined picture parameters and sometimes can not determine the boundaries of objects.

Janelle notes that, while the neural network was trained on a narrow set of data in the form of birds, it obtained nice images:


However, when the creators trained it on a dataset that included pictures from sheep to shopping centers, it could not create a meaningful image in a similar way. The author of AI Weirdness believes that the error lies in too wide a set of initial data, in which AttnGAN could not select the appropriate instances:


In addition, it somehow has a problem with determining the correct number of holes on the human face. Developers AttnGAN added to the control dataset person celebrities to create photorealistic portraits, but the neural network couldn't do that:


Additionally, neural network is real bad at displaying animals:


Janelle Shane calls the project AttnGAN "Visual Chatbot on the contrary." This chat bot analyzes the image that the user sends and describes it, often implausibly.

Facebook to Release PyTorch 1.0

This release added support for large cloud platforms, a C ++ interface, a set of JIT compilers
10 December 2018   106

Facebook has released a stable version of the library for machine learning PyTorch 1.0. This iteration added support for large cloud platforms, a C ++ interface, a set of JIT compilers, and various improvements.

The stable version received a set of JIT compilers that eliminate the dependence of the code on the Python interpreter. The model code is transformed into Torch Script - a superstructure over Python. Keeping the opportunity to work with the model in the Python environment, the user can download it to other projects not related to this language. So, the PyTorch developers state that the code processed in this way can be used in the C ++ API.

The torch.distributed package and the torch.nn.parallel.DistributedDataParallel module are completely redesigned. torch.distributed now has better performance and works asynchronously with the Gloo, NCCL and MPI libraries.

The developers added a C ++ wrapper to PyTorch 1.0. It contains analogs of Python interface components, such astorch.nn,torch.optim, According to the creators, the new interface should provide high performance for C ++ applications. True, the C ++ API is still experimental, but it can be used in projects now.

To improve the efficiency of working with PyTorch 1.0, a Torch Hub repository has been created, which stores pre-trained models of neural networks. You can publish your own development using the file, after which the model will be available for download by any user via the torch.hub.load API.

Support for C extensions and the module torch.utils.trainer were removed from the library.

Facebook released the preliminary version of PyTorch 1.0 at the beginning of October 2018, and in two months the developers brought the framework to a stable state.