[ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

A list of popular deep learning models related to classification, segmentation and detection problems

ProxylessNAS-Pytorch

No description available

my implementation of ProxylessNAS

Reproducing the ProxylessNAS approach and making some improvements (ongoing)

Repetition of ProxylessNAS Including Search

inspired by proxylessNAS and GDAS, binary path and Gumbel-softmax applied on network level search space (fabric)

Replication of Neural Predictor for Neural Architecture Search with Tensorflow

To Understand ProxylessNAS (https://github.com/mit-han-lab/proxylessnas)

My implement of the ProxylessNAS

No description available

Pytorch implementation of ProxylessNASNets, including ProxylessNASNet-GPU/CPU/Mobile.

Code based on ProxylessNAS in MIT Han Lab

No description available

Modifying the ProxyLessNAS paper (https://arxiv.org/pdf/1812.00332.pdf) to implement the super architecture presented in DENSE (https://arxiv.org/pdf/2001.08055.pdf)

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware, implementation with CIFAR10/100 dataset using Microsoft NNI

No description available

No description available

No description available

No description available

# ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware [[arXiv]](https://arxiv.org/abs/1812.00332) [[Poster]](https://file.lzhu.me/projects/proxylessNAS/figures/ProxylessNAS_iclr_poster_final.pdf) - Third place in the Low Power Image Recognition Challenge (LPIRC), classification track, @CVPR 2019 ## Performance Without any proxy, directly and efficiently search neural network architectures on your target **task** and **hardware**!