Neural Architecture Search
776 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
Proximal Policy Optimization Algorithms
labmlai/annotated_deep_learning_paper_implementations
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We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.
Searching for MobileNetV3
tensorflow/models
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ICCV 2019
We achieve new state of the art results for mobile classification, detection and segmentation.
DARTS: Differentiable Architecture Search
This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner.
Efficient Neural Architecture Search via Parameter Sharing
google-research/google-research
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The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set.
MnasNet: Platform-Aware Neural Architecture Search for Mobile
In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency.
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
MIT-HAN-LAB/ProxylessNAS
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ICLR 2019
We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set.
EfficientNetV2: Smaller Models and Faster Training
google/automl
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By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87. 3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2. 0% accuracy while training 5x-11x faster using the same computing resources.
Progressive Neural Architecture Search
tensorflow/models
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ECCV 2018
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms.
Learning Transferable Architectures for Scalable Image Recognition
tensorflow/models
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CVPR 2018
In our experiments, we search for the best convolutional layer (or "cell") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, named "NASNet architecture".
