83 Open source projects that are alternatives of or similar to Nsga Net

Fast and flexible AutoML with learning guarantees.

Neural Architecture Search Powered by Swarm Intelligence 🐜

A curated list of awesome architecture search resources

a distributed Hyperband implementation on Steroids

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

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

AutoGluon: AutoML for Text, Image, and Tabular Data

AutoML frAmework for Neural Networks

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

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

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

my own deep learning mastery roadmap

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

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

"NAS evaluation is frustratingly hard", ICLR2020

real-time network architecture for mobile devices and semantic segmentation

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

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

SGAS: Sequential Greedy Architecture Search (CVPR'2020) https://www.deepgcns.org/auto/sgas

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

AutoML library for deep learning

Large scale and asynchronous Hyperparameter Optimization at your fingertip.

Code for the neural architecture search methods contained in the paper Efficient Forward Neural Architecture Search

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

Genetic neural architecture search with Keras

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

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

TensorFlow implementation of PNASNet-5 on ImageNet

Federated Learning Library: https://fedml.ai

Code for Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells, CVPR '19

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

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

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

Bridging the gap Between Stability and Scalability in Neural Architecture Search

Differentiable architecture search for convolutional and recurrent networks

Automated deep learning algorithms implemented in PyTorch.

PyTorch implementation of PNASNet-5 on ImageNet

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

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

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

Reproducible Rapid Research for Neural Architecture Search (NAS)

Block-wisely Supervised Neural Architecture Search with Knowledge Distillation (CVPR 2020)

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

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

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

DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

Encodings for neural architecture search

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

Implement Differentiable Architecture Search (DARTS) for RNN with fastai

Single Path One-Shot NAS MXNet implementation with full training and searching pipeline. Support both Block and Channel Selection. Searched models better than the original paper are provided.

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

Fast & Simple Resource-Constrained Learning of Deep Network Structure

Learnable Embedding Space for Efficient Neural Architecture Compression

A general, modular, and programmable architecture search framework

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

aw_nas: A Modularized and Extensible NAS Framework

DeepArchitect: Automatically Designing and Training Deep Architectures

A curated list of automated deep learning (including neural architecture search and hyper-parameter optimization) resources.

This directory contains code necessary to run the GraphNAS algorithm.

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.