danielegrattarola/GINR
Code for "Generalised Implicit Neural Representations" (NeurIPS 2022).
This project helps scientists and researchers analyze and understand complex data that exists on 3D surfaces or across time, like protein structures, weather patterns, or reaction-diffusion simulations. It takes raw data from these irregular surfaces or time series and generates high-resolution, interpolated visualizations and predictions. The primary users are researchers in fields like computational biology, environmental science, or physics who need to reconstruct and explore continuous signals from sparse measurements.
No commits in the last 6 months.
Use this if you need to generate high-resolution surface plots or animations from sparse data points on 3D objects or over time, especially for physical phenomena.
Not ideal if your data is simple tabular data or if you need to analyze discrete events rather than continuous signals on complex geometries.
Stars
74
Forks
5
Language
HTML
License
—
Category
Last pushed
Feb 07, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/danielegrattarola/GINR"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Westlake-AI/openmixup
CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark
YU1ut/MixMatch-pytorch
Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"
kamata1729/QATM_pytorch
Pytorch Implementation of QATM:Quality-Aware Template Matching For Deep Learning
nttcslab/msm-mae
Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representations
rgeirhos/generalisation-humans-DNNs
Data, code & materials from the paper "Generalisation in humans and deep neural networks" (NeurIPS 2018)