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

Keskeiset käsitteet

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

Tiivistelmä

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.

1. Introduction to Neural Architecture Search (NAS)

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

2. Modularizing Search Space into Blocks

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

3. DNA: Distillation via Supervising Learning

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

4. DNA+: Distillation via Progressive Learning

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

5. 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.

Tilastot

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.

Lainaukset

"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."

Tärkeimmät oivallukset

DNA Family

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

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

Syvällisempiä Kysymyksiä

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