Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.

Related tags

Deep Learningaitensorflowkerasgenerative-modeltensorflow2
Overview

Sarus published models

Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.

The required packages are managed with pipenv and can be installed using pipenv install. Please see the pipenv documentation for more information.

Philosophy

These models' implementations are intended to be easy to read and to adapt by making use of the latest Tensorflow 2 library and Keras API.

Basic usage

To install and train a model.

pipenv install
pipenv shell
python train.py

To visualize losses and reconstructions.

tensorboard --logdir ./logs/

Available models

Owner
Sarus Technologies
Sarus Technologies
GitHub Repository https://sarus.tech/
Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM)

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Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks

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SGoLAM - Simultaneous Goal Localization and Mapping

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An experimental technique for efficiently exploring neural architectures.

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Implementation of our recent paper, WOOD: Wasserstein-based Out-of-Distribution Detection.

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WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution

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Image-to-image translation with conditional adversarial nets

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