83 Open source projects that are alternatives of or similar to Graphnas

Implement Differentiable Architecture Search (DARTS) for RNN with fastai

[ICML2020] "AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks" by Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan Lin, Zhangyang Wang

Basic implementation of [Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578).

Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding. NeurIPS 2020.

A research library for pytorch-based neural network pruning, compression, and more.

Federated Learning Library: https://fedml.ai

Learnable Embedding Space for Efficient Neural Architecture Compression

Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search https://arxiv.org/abs/1807.06906

Parallel random matrix tools and complexity for deep learning

PyTorch implementation of PNASNet-5 on ImageNet

NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

A PyTorch implementation of NASBench

PaddleSlim is an open-source library for deep model compression and architecture search.

Encodings for neural architecture search

[ECCV2020] NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

[ICLR 2020] "FasterSeg: Searching for Faster Real-time Semantic Segmentation" by Wuyang Chen, Xinyu Gong, Xianming Liu, Qian Zhang, Yuan Li, Zhangyang Wang

A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.

Automated deep learning algorithms implemented in PyTorch.

H. Zhang, Q. Yao, M. Yang, Y. Xu, X. Bai. AutoSTR: Efficient Backbone Search for Scene Text Recognition. European Conference on Computer Vision (ECCV). 2020.

Differentiable architecture search for convolutional and recurrent networks

A toolbox for receptive field analysis and visualizing neural network architectures

Fast & Simple Resource-Constrained Learning of Deep Network Structure

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

A curated list of automated machine learning papers, articles, tutorials, slides and projects

Neural Architecture Search Powered by Swarm Intelligence 🐜

Automatic DNN compression tool with various model compression and neural architecture search techniques

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

PyTorch-style and human-readable RegNet with a spectrum of pre-trained models

A curated list of awesome architecture search resources

The official repo for CVPR2021——ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search.

Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"

Large scale and asynchronous Hyperparameter Optimization at your fingertip.

Multi-Task Learning Framework on PyTorch. State-of-the-art methods are implemented to effectively train models on multiple tasks.

TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search (ECCV2020)

a distributed Hyperband implementation on Steroids

(ICCV 2021) BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search

AutoML library for deep learning

my own deep learning mastery roadmap

[ICCV 2019] "AutoGAN: Neural Architecture Search for Generative Adversarial Networks" by Xinyu Gong, Shiyu Chang, Yifan Jiang and Zhangyang Wang

PC-DARTS (PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search, published in ICLR 2020) implemented in Tensorflow 2.0+. This is an unofficial implementation.

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

This repo is about NAS

Fast and flexible AutoML with learning guarantees.

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

Minimal Tensorflow implementation of the paper "Neural Architecture Search With Reinforcement Learning" presented at ICLR 2017

CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification (ICCV2021)

real-time network architecture for mobile devices and semantic segmentation

An AutoDL tool for Neural Architecture Search and Hyperparameter Optimization on Tensorflow and Keras

[ICLR 2021] "Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective" by Wuyang Chen, Xinyu Gong, Zhangyang Wang

Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)

AutoGluon: AutoML for Text, Image, and Tabular Data

Official PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)

Genetic neural architecture search with Keras

Pytorch Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"

TensorFlow implementation of PNASNet-5 on ImageNet

[CVPR 2020] When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks

[CVPR 2020] MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning

Slimmable Networks, AutoSlim, and Beyond, ICLR 2019, and ICCV 2019

Reproducible Rapid Research for Neural Architecture Search (NAS)