ニューラルアーキテクチャ探索（NAS）

Neural Architecture Search (NAS) is a sophisticated technique within the realm of Automated Machine Learning (AutoML) that automates the design of artificial neural networks. Traditionally, engineering high-performance deep learning (DL) architectures required extensive human expertise, intuition, and time-consuming trial-and-error. NAS replaces this manual process with algorithmic strategies that systematically explore a vast range of network topologies to discover the optimal structure for a specific task. By testing various combinations of layers and operations, NAS can identify architectures that significantly outperform human-designed models in terms of accuracy, computational efficiency, or inference speed.

Core Mechanisms of NAS

The process of discovering a superior architecture generally involves three fundamental dimensions that interact to find the best neural network (NN):

1. Search Space: This defines the set of all possible architectures the algorithm can explore. It acts like a library of building blocks, such as convolution filters, pooling layers, and various activation functions. A well-defined search space constrains complexity to ensure the search remains computationally feasible while allowing enough flexibility for innovation.
2. Search Strategy: Instead of testing every possibility (brute force), NAS employs intelligent algorithms to navigate the search space efficiently. Common approaches include reinforcement learning, where an agent learns to generate better architectures over time, and evolutionary algorithms, which mutate and combine top-performing models to breed superior candidates.
3. Performance Estimation Strategy: Training every candidate network from scratch is prohibitively slow. To accelerate this, NAS uses estimation techniques—such as training on fewer epochs, using lower-resolution proxy datasets, or employing weight sharing—to quickly rank the potential of a candidate architecture.

実際のアプリケーション

NAS has become critical in industries where hardware constraints or performance requirements are strict, pushing the boundaries of computer vision (CV) and other AI domains.

• Efficient Edge Computing: Deploying AI on mobile devices requires models that are both lightweight and fast. NAS is extensively used to discover architectures like MobileNetV3 and EfficientNet that minimize inference latency while maintaining high precision. This is vital for edge AI applications, such as real-time video analytics on smart cameras or autonomous drones.
• Medical Imaging: In medical image analysis, accuracy is paramount. NAS can tailor networks to detect subtle anomalies in X-rays or MRI scans, often finding novel feature extraction pathways that human engineers might overlook. This leads to more reliable tools for identifying conditions like brain tumors or fractures with higher sensitivity.

NASと関連概念

NASの具体的な役割を理解するには、類似の最適化手法との違いを区別することが有用である：

• NASとハイパーパラメータ調整：どちらも最適化を伴うが、 ハイパーパラメータ調整は 固定されたアーキテクチャに対し、 学習率 やバッチサイズなどの設定値を調整することに焦点を当てる。 一方、NASは層の数やニューロンの接続方法など、 モデル自体の根本的な構造そのものを変更する。
• NAS対転移学習： 転移学習は既存の事前学習済みモデルを用い、 その重みを新たなタスクに適応させる。 NASは学習開始前にモデルアーキテクチャを一から構築する（またはより優れたバックボーンを探索する）。

NAS由来モデルの活用

NASによる完全な探索を実行するにはGPU リソースが必要ですが、開発者はNASを通じて作成されたモデルを容易に利用できます。例えば、YOLOアーキテクチャYOLO、物体検出タスク向けに最適化するため、これらの探索原理を用いて発見されました。

Python 、事前学習済みNASモデルをロードして使用する方法を示しています。 ultralytics パッケージで提供される：

from ultralytics import NAS

# Load a pre-trained YOLO-NAS model (architecture found via NAS)
# 'yolo_nas_s.pt' refers to the small version of the model
model = NAS("yolo_nas_s.pt")

# Run inference on an image to detect objects
# This utilizes the optimized architecture for fast detection
results = model("https://ultralytics.com/images/bus.jpg")

# Print the top detected class
print(f"Detected: {results[0].names[int(results[0].boxes.cls[0])]}")

For those looking to train state-of-the-art models without the complexity of NAS, the Ultralytics YOLO26 offers a highly optimized architecture out of the box, incorporating the latest advancements in research. You can easily manage datasets, training, and deployment for these models using the Ultralytics Platform, which simplifies the entire MLOps lifecycle.