Single/multi view image(s) to voxel reconstruction using a recurrent neural network
This project is a generative adversarial network implementation and research framework. It provides the tools and hyperparameters necessary to train and evaluate generative models across various datasets, specifically designed to reproduce results from academic research. The framework includes a Parzen density likelihood estimator to calculate model log likelihood. This allows for the quantitative evaluation of generative distributions and the measurement of overall model performance. The codebase covers machine learning research capabilities, focusing on the training of adversarial networks
Source code release of the paper: Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation.
TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training"
Source code for "Understanding Deep Image Representations by Inverting Them", CVPR 2015
The main features of aravindhm/deep-goggle are: Computer Vision Applications.
Open-source alternatives to aravindhm/deep-goggle include: chrischoy/3d-r2n2 — Single/multi view image(s) to voxel reconstruction using a recurrent neural network. goodfeli/adversarial — This project is a generative adversarial network implementation and research framework. It provides the tools and… guanghan/gnet-pose — Source code release of the paper: Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation. hep-lbdl/adversarial-jets — Training, generation, and analysis code for Learning Particle Physics by Example: Location-Aware Generative… lantaoyu/seqgan — Implementation of Sequence Generative Adversarial Nets with Policy Gradient. carpedm20/simulated-unsupervised-tensorflow — TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training".