Single Image Random Dot Stereogram for Tensorflow

Last update: Aug 10, 2022

Overview

TensorFlow-SIRDS

Single Image Random Dot Stereogram for Tensorflow

SIRDS is a means to present 3D data in a 2D image. It allows for scientific data display of a waterfall type of plot with no hidden lines due to perspective. Data is encoded to show data in the Z direction as depth.

This project is based upon: https://www.cs.waikato.ac.nz/~ihw/papers/94-HWT-SI-IHW-SIRDS-paper.pdf

Sections

SIRDS usage demo for TensorFlow Op

Owner

Greg Peatfield

GitHub Repository

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Single Image Random Dot Stereogram for Tensorflow

Related tags

Overview

TensorFlow-SIRDS

Sections

Owner

Greg Peatfield

Dataset for the Research2Clinics @ NeurIPS 2021 Paper: What Do You See in this Patient? Behavioral Testing of Clinical NLP Models

TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training"

An example of Scatterbrain implementation (combining local attention and Performer)

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Symbolic Music Generation with Diffusion Models

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MakeItTalk: Speaker-Aware Talking-Head Animation

Research code for the paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models"

PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet

Deep learning with TensorFlow and earth observation data.

This is the official implementation of TrivialAugment and a mini-library for the application of multiple image augmentation strategies including RandAugment and TrivialAugment.

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