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.

Last update: Aug 19, 2022

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

GitHub Repository https://sarus.tech/

An official PyTorch Implementation of Boundary-aware Self-supervised Learning for Video Scene Segmentation (BaSSL)

72 Dec 28, 2022

[NeurIPS-2021] Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data

MosaicKD Code for NeurIPS-21 paper "Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data" 1. Motivation Natural images share common l

37 Nov 10, 2022

Implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

PRP Introduction This is the implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

39 Dec 29, 2022

Submodular Subset Selection for Active Domain Adaptation (ICCV 2021)

S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation ICCV 2021 Harsh Rangwani, Arihant Jain*, Sumukh K Aithal*, R. Ve

13 Dec 28, 2022

Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models

Explainable_FIQA_WITH_AMVA Note This is the official repository of the paper: Explainability of the Implications of Supervised and Unsupervised Face I

3 May 08, 2022

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

Overview

Sarus published models

Philosophy

Basic usage

Available models

Owner

Sarus Technologies

An official PyTorch Implementation of Boundary-aware Self-supervised Learning for Video Scene Segmentation (BaSSL)

[NeurIPS-2021] Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data

Implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

Submodular Subset Selection for Active Domain Adaptation (ICCV 2021)

Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models

Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders

The-Secret-Sharing-Schemes - This interactive script demonstrates the Secret Sharing Schemes algorithm

Create images and texts with the First Order Generative Adversarial Networks

[CVPR'2020] DeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Data

Code release for Hu et al. Segmentation from Natural Language Expressions. in ECCV, 2016

Unofficial TensorFlow implementation of the Keyword Spotting Transformer model

Official implementation for the paper: Generating Smooth Pose Sequences for Diverse Human Motion Prediction

AntiFuzz: Impeding Fuzzing Audits of Binary Executables

This is a JAX implementation of Neural Radiance Fields for learning purposes.

Make differentially private training of transformers easy for everyone

Hashformers is a framework for hashtag segmentation with transformers.

[NeurIPS 2020] Official Implementation: "SMYRF: Efficient Attention using Asymmetric Clustering".

Segmentation vgg16 fcn - cityscapes

Implement some metaheuristics and cost functions

A Python package for performing pore network modeling of porous media