DNA Family: Boosting Weight-Sharing NAS with Block-Wise Supervisions

Core Concepts

DNA family models improve weight-sharing NAS efficiency and effectiveness by modularizing the search space into blocks and utilizing distilling neural architecture techniques.

Abstract

The content discusses the challenges of weight-sharing NAS and introduces the DNA family models as a solution. It covers the methodology of DNA, DNA+, and DNA++, along with their applications and benefits in neural architecture search. Experimental results and comparisons with state-of-the-art models are provided.

Introduction to Neural Architecture Search (NAS)

NAS aims to automate neural architecture design.
Weight-sharing NAS improves search efficiency but faces effectiveness challenges.

Modularizing Search Space into Blocks

Dividing the search space into blocks reduces search complexity.
Training each block separately enhances architecture ranking.

DNA: Distillation via Supervising Learning

DNA models use distillation techniques for architecture search.
Supervised learning with teacher guidance improves search efficiency.

DNA+: Distillation via Progressive Learning

DNA+ iteratively scales searched architectures to improve performance.
Progressive learning enhances regularization and model accuracy.

DNA++: Distillation via Self-Supervised Learning

DNA++ uses self-supervision to reduce architecture ranking bias.
Self-supervised learning combined with weight-sharing NAS improves search effectiveness.

Stats

Extensive experimental evaluations show DNA models achieve top-1 accuracy of 78.9% on ImageNet.
The search space contains 2 × 10^17 architectures for MBConv models.
DNA models outperform state-of-the-art NAS models in terms of accuracy and efficiency.

Quotes

"Our proposed DNA models can rate all architecture candidates, resolving scalability, efficiency, and compatibility dilemmas."
"DNA++ introduces new SSL techniques to prevent mode collapse and encourage output divergence among samples."

Key Insights Distilled From

DNA Family

by Guangrun Wan... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01326.pdf

Deeper Inquiries

질문 1

DNA 패밀리 모델을 다른 데이터셋과 작업에 대해 어떻게 더 최적화할 수 있을까요?
답변 1 여기에

질문 2

DNA 방법론과 전통적인 NAS 방법론을 비교할 때 DNA 접근법의 잠재적인 제한 사항이 무엇인가요?
답변 2 여기에

질문 3

모듈러 학습과 자기 지도 학습의 개념을 기계 학습 연구의 다른 영역에 어떻게 적용할 수 있을까요?
답변 3 여기에