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

Utility code for use with PyXLL

scikit-learn inspired API for CRFsuite

PyTorch implemention of ICCV'21 paper SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

Here we present the implementation in TensorFlow of our work about liver lesion segmentation accepted in the Machine Learning 4 Health Workshop

Convert BART models to ONNX with quantization. 3X reduction in size, and upto 3X boost in inference speed

Image classification for projects and researches

ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs

Meta-learning for NLP

LV-BERT: Exploiting Layer Variety for BERT (Findings of ACL 2021)

Official public repository of paper "Intention Adaptive Graph Neural Network for Category-Aware Session-Based Recommendation"

CoINN: Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels

Point-NeRF: Point-based Neural Radiance Fields

SCU OlympicsRunning Baseline

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[ ICCV 2021 Oral ] Our method can estimate camera poses and neural radiance fields jointly when the cameras are initialized at random poses in complex scenarios (outside-in scenes, even with less texture or intense noise )