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unsupervised-2017-cvprw - Disentangling Motion, Foreground and Background Features in Videos

  •    Python

This repo contains the source codes for our work as in title. Please refer to our project webpage or original paper for more details. This project requires UCF-101 dataset and its localization annotations (bonding box for action region). Please note that the annotations only contain bounding boxes for 24 classes out of 101. We only use these 24 classes for further experiments.

goNEAT - The GOLang implementation of NeuroEvolution of Augmented Topologies (NEAT) method to grow and teach Artificial Neural Networks without back propagation

  •    Go

This repository provides implementation of NeuroEvolution of Augmenting Topologies (NEAT) method written in Go language. The Neuroevolution (NE) is an artificial evolution of Neural Networks (NN) using genetic algorithms in order to find optimal NN parameters and topology. Neuroevolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT method implemented in this work do search for both: optimal connections weights and topology for given task (number of NN nodes per layer and their interconnections).

goNEAT_NS - This project provides GOLang implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization aimed to solve deceptive tasks with strong local optima

  •    Go

This repository provides implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization implemented in GoLang. The Neuro-Evolution (NE) is an artificial evolution of Neural Networks (NN) using genetic algorithms in order to find optimal NN parameters and topology. Neuro-Evolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT method implemented in this work do search for both: optimal connections weights and topology for given task (number of NN nodes per layer and their interconnections).






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