SemiNAS: Semi-Supervised Neural Architecture Search

Last update: Aug 31, 2022

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Overview

SemiNAS: Semi-Supervised Neural Architecture Search

This repository contains the code used for Semi-Supervised Neural Architecture Search, by Renqian Luo, Xu Tan, Rui Wang, Tao Qin, En-Hong Chen, Tie-Yan Liu.

Environments and Requirements

The code is built and tested on Pytorch 1.2

NASBench-101 Experiments

Please refer to nasbench/README.md

ImageNet Experiments

Please refer to imagenet/README.md

Owner

Renqian Luo

Researcher @ Microsoft Research Asia

GitHub Repository

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SemiNAS: Semi-Supervised Neural Architecture Search

Related tags

Overview

SemiNAS: Semi-Supervised Neural Architecture Search

Environments and Requirements

NASBench-101 Experiments

ImageNet Experiments

Owner

Renqian Luo

Object detection (YOLO) with pytorch, OpenCV and python

[WACV 2020] Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints

The codes of paper 'Active-LATHE: An Active Learning Algorithm for Boosting the Error exponent for Learning Homogeneous Ising Trees'

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This is the official pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering" on VQA Task

A multilingual version of MS MARCO passage ranking dataset

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