Open-source alternatives to Kinetics I3d

The author's officially unofficial PyTorch BigGAN implementation.

Tensorflow code for the Bayesian GAN (https://arxiv.org/abs/1705.09558) (NIPS 2017)

NeurIPS 2021 Spotlight Official implementation of Long Short-Term Transformer for Online Action Detection

G-SchNet is a generative neural network that samples molecules by sequentially placing atoms in 3d space. It can be trained on data sets of 3d molecules with variable sizes and compositions. The conditional version, cG-SchNet, explicitly takes chemical and structural properties into account to…

Fast Neural Style for Image Style Transform by Pytorch

Software and pre-trained models for automatic photo quality enhancement using Deep Convolutional Networks

Tensorflow implementation for reproducing main results in the paper Self-Attention Generative Adversarial Networks by Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena.

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

This is a TensorFlow implementation of the Deep Convolutional Generative Adversarial Network (DCGAN) architecture, providing a framework for training generative models that produce synthetic images from random noise vectors. The project implements the core DCGAN design, using transposed convolutions for upsampling, batch normalization for training stability, and leaky ReLU activations in the discriminator, all executed as static TensorFlow computation graphs. The implementation supports training on custom image datasets by accepting user-supplied image folders without requiring a predefined f

PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

This code implements the video- and sensor-based action segmentation models from Temporal Convolutional Networks for Action Segmentation and Detection by Colin Lea, Michael Flynn, Rene Vidal, Austin Reiter, Greg Hager arXiv 2016 (in-review).

pytorch implementation of fast-neural-style

A TensorFlow implementation of the Differentiable Neural Computer.

Code for the paper "Adversarial Generator-Encoder Networks"

This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior to reconstruct images from noisy or incomplete data. It functions as a neural network image prior, utilizing the inherent biases of the network architecture to restore pixels without the need for a pre-trained dataset or external learning. The system performs zero-shot image restoration by treating the network architecture itself as a regularization term. It uses a randomly initialized encoder-decoder structure and iterative gradient descent to minimize pixel-wise loss, recove

An efficient video loader for deep learning with smart shuffling that's super easy to digest