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

Naszilla is a Python library for neural architecture search (NAS)

A lightweight and easy-to-use deep learning framework with neural architecture search.

NSGA-Net, a Neural Architecture Search Algorithm

aw_nas: A Modularized and Extensible NAS Framework

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

DeepArchitect: Automatically Designing and Training Deep Architectures

Bridging the gap Between Stability and Scalability in Neural Architecture Search

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

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.

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

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

"NAS evaluation is frustratingly hard", ICLR2020

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

AutoML frAmework for Neural Networks

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

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

A general, modular, and programmable architecture search framework

This directory contains code necessary to run the GraphNAS algorithm.

TensorFlow implementation of PNASNet-5 on ImageNet

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

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

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

Automated deep learning algorithms implemented in PyTorch.