Neural Architecture Search
774 papers with code • 26 benchmarks • 27 datasets
Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures.
Image Credit : NAS with Reinforcement Learning
Benchmarks
These leaderboards are used to track progress in Neural Architecture Search
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Libraries
Use these libraries to find Neural Architecture Search models and implementationsDatasets
CIFAR-10
ImageNet
CIFAR-100
MNIST
Oxford 102 Flower
STL-10
Stanford Cars
DTD
Food-101
FGVC-Aircraft
Most implemented papers
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
dstamoulis/single-path-nas
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Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the runtime constraint of a mobile device?
ISyNet: Convolutional Neural Networks design for AI accelerator
To address this problem we propose a measure of hardware efficiency of neural architecture search space - matrix efficiency measure (MEM); a search space comprising of hardware-efficient operations; a latency-aware scaling method; and ISyNet - a set of architectures designed to be fast on the specialized neural processing unit (NPU) hardware and accurate at the same time.
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
open-mmlab/mmdetection
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CVPR 2019
Here we aim to learn a better architecture of feature pyramid network for object detection.
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
yuhuixu1993/PC-DARTS
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ICLR 2020
Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture.
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file.
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
The original ImageNet dataset is a popular large-scale benchmark for training Deep Neural Networks.
ReNAS: Relativistic Evaluation of Neural Architecture Search
huawei-noah/Efficient-Computing
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CVPR 2021
An effective and efficient architecture performance evaluation scheme is essential for the success of Neural Architecture Search (NAS).
Single Path One-Shot Neural Architecture Search with Uniform Sampling
open-mmlab/mmrazor
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ECCV 2020
It is easy to train and fast to search.
Searching for A Robust Neural Architecture in Four GPU Hours
D-X-Y/NAS-Projects
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CVPR 2019
To avoid traversing all the possibilities of the sub-graphs, we develop a differentiable sampler over the DAG.
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
mindspore-ai/models
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CVPR 2021
To achieve an extremely fast NAS while preserving the high accuracy, we propose to identify the vital blocks and make them the priority in the architecture search.
