Neural Architecture Search
780 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
Libraries
Use these libraries to find Neural Architecture Search models and implementationsDatasets
Most implemented papers
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
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
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
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
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
It is easy to train and fast to search.
Searching for A Robust Neural Architecture in Four GPU Hours
To avoid traversing all the possibilities of the sub-graphs, we develop a differentiable sampler over the DAG.
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
Here we propose a novel frame level FAS method based on Central Difference Convolution (CDC), which is able to capture intrinsic detailed patterns via aggregating both intensity and gradient information.