PyTorch implementation 3D U-Net and its variants.
The code allows for training the U-Net for both: semantic segmentation (binary and multi-class) and regression problems (e.g. …
Keywords: Unet, U-net, 3D U-net, semantic segmentation, computer vision, PyTorch, de-noising, deep learning, neural networks
Reference implementation for the paper Adversarial Latent Autoencoders by S. Pidhorskyi, D. A. Adjeroh, and G. Doretto, CVPR 2020.
Keywords: ALAE, StyleALAE, adversarial latent autoencoders, computer vision, image processing, deep learning, autoencoder, unsupervised learning
The open source implementation of DeepMind's AlphaFold v2 award winning protein folding algorithm.
Keywords: protein folding, machine learning, neural networks, deep learning, CASP14, structure prediction, DeepMind, JAX, TensorFlow
This is the Open source of the paper "AnimeGAN: a novel lightweight GAN for photo animation", which uses the GAN framwork to transform real-world photos …
Keywords: AnimeGAN, Tensorflow, generative adversarial networks, deep learning, computer vision, anime, photo animation
Asteroid is a Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code that supports a large …
Keywords: Asteroid, Pytorch, audio source separation, toolkit, audio, sound processing, ConvTasnet, Tasnet, Deep clustering, DualPathRNN, Chimera++, Wavesplit
The official implementation of a method for multiple instance learning using deep neural networks and attention mechanism.
Keywords: multiple instance learning, deep learning, attention, PyTorch, weak classification
The reference implementation for the attri2vec algorithm.
Keywords: Attri2vec, representation learning, graph machine learning, node classification, link prediction
AugLy is a data augmentations library that currently supports four modalities (audio, image, text & video) and over 100 augmentations. Each modality’s augmentations are contained …
Keywords: data augmentation, machine learning, Python, PyTorch
Backprop is a Python library for fine-tuning and deploying machine learning models. It includes pre-trained models for natural language processing, e.g., question answering, text summarisation, …
Keywords: neural networks, pre-trained models, fine-tuning, library, natural language processing, computer vision, Python, PyTorch
A framework for benchmarking the performance of graph neural network algorithms including datasets.
Keywords: graph neural network, machine learning, datasets, benchmark, graph convolutions, PyTorch