Continual learning is the problem of learning from a nonstationary stream of data, a fundamental issue for sustainable and efficient training of deep neural networks over time. Unfortunately, deep learning libraries only provide primitives for offline training, assuming that model's architecture and data are fixed. Avalanche is an open source library maintained by the ContinualAI non-profit organization that extends PyTorch by providing first-class support for dynamic architectures, streams of datasets, and incremental training and evaluation methods. Avalanche provides a large set of predefined benchmarks and training algorithms and it is easy to extend and modular while supporting a wide range of continual learning scenarios. Documentation is available at \url{https://avalanche.continualai.org}.

A recent research paper published by InterDigital AI Lab introduces CompressAI. CompressAI is a platform that provides custom operations, layers, models, and tools to research, develop, and evaluate end-to-end image and video compression codecs. It uses pre-trained models and evaluation tools to compare learned methods with traditional codecs. Various models have been trained on learned end-to-end compression from scratch and re-implemented in PyTorch. Artificial Neural Network (ANN) based codecs have shown remarkable outcomes for compressing images.

Hi everyone, I've recently built Mimicry, a PyTorch library for GANs which I hope can make GAN research findings more reproducible. The general idea is to have an easily accessible set of implementations (that reproduce the original scores as closely as possible), baseline scores for comparisons, and metrics for GANs which researchers can quickly use to produce results and compare. For reproducibility, I re-implemented the original models and verified their correctness by checking their scores against the reported ones under the same training and evaluation conditions. On the metrics part, to ensure backward compatibility of existing scores, I adopted the original TensorFlow implementations of Inception Score, FID, and KID so new scores produced can be compared with other works directly. I've also included a tutorial to implement a more sophisticated GAN like Self-supervised GAN (SSGAN) from the ground up, again with a focus on reproducing the results.

PyTorch is an Artificial Intelligence library that has been created by Facebook's artificial intelligence research group . The source code is accessible on GitHub and it becomes more popular day after day with more than 33.4kstars and 8.3k. This PyTorch is getting a lot of consideration since 2017 and is in constant adoption increase. Now let's see this in action on how to create a neural network with PyTorch: PyTorch has an official style for you to design and build your neural network. The complete explanation or definition should stay inside an object (OOP) that is a child of the class nn.Module.

PyTorch is a Python-based scientific computing package that uses the power of graphics processing units. It is also one of the preferred deep learning research platforms built to provide maximum flexibility and speed. It is known for providing two of the most high-level features; namely, tensor computations with strong GPU acceleration support and building deep neural networks on a tape-based autograd systems. There are many existing Python libraries which have the potential to change how deep learning and artificial intelligence are performed, and this is one such library. One of the key reasons behind PyTorch's success is it is completely Pythonic and one can build neural network models effortlessly.