Popular GitHub Repositories for Implicit Layers

Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun

simple neural network library in ANSI C

A library for differentiable nonlinear optimization

This repository contains the code for the paper "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization"

Acceleration package for neural networks on multi-core CPUs

[CVPR'22] ICON: Implicit Clothed humans Obtained from Normals

A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods

A comprehensive list of Implicit Representations and NeRF papers relating to Robotics/RL domain, including papers, codes, and related websites

[CVPR'22] NICE-SLAM: Neural Implicit Scalable Encoding for SLAM

Learning Continuous Image Representation with Local Implicit Image Function, in CVPR 2021 (Oral)

This repository contains the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"

Point-NeRF: Point-based Neural Radiance Fields

A simple and extensible library to create Bayesian Neural Network layers on PyTorch.

kapre: Keras Audio Preprocessors

A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

This project extends the idea of the innovative architecture of Kolmogorov-Arnold Networks (KAN) to the Convolutional Layers, changing the classic linear transformation of the convolution to learnable non linear activations in each pixel.

Multi-view 3D reconstruction using neural rendering. Unofficial implementation of UNISURF, VolSDF, NeuS and more.

[ECCV 2024 - Oral] ACE0 is a learning-based structure-from-motion approach that estimates camera parameters of sets of images by learning a multi-view consistent, implicit scene representation.

Shape and dimension inference (Keras-like) for PyTorch layers and neural networks