Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search

Core Concepts

The author proposes a Pareto-wise end-to-end ranking classifier to simplify the architecture search process in multi-objective NAS, addressing the rank disorder issue and outperforming other methods.

Abstract

In the content, the authors introduce a novel approach using a Pareto-wise ranking classifier to streamline multi-objective NAS. By transforming the complex task into a simple classification problem, they alleviate the rank disorder issue and achieve superior results compared to existing methods. The proposed method successfully identifies promising network architectures under various objectives and constraints.

Stats

The proposed approach consumes only 1.19 GPU days on ImageNet.
CENAS-A achieves higher accuracy than manual design models with smaller #Params.
CENAS-C outperforms RL-based methods in terms of accuracy.

Quotes

"The proposed approach is able to alleviate the rank disorder issue and outperforms other methods."
"The proposed method is able to find a set of promising network architectures with different model sizes ranging from 2M to 5M under diverse objectives and constraints."

Key Insights Distilled From

Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search

by Lianbo Ma,Na... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2109.07582.pdf

Deeper Inquiries

How can the concept of ordinal optimization theory be applied in other areas beyond neural architecture search

The concept of ordinal optimization theory can be applied in various areas beyond neural architecture search. One potential application is in hyperparameter optimization for machine learning algorithms. By focusing on the order of performance rather than exact values, ordinal optimization can simplify the search process and reduce computational costs. This approach could be particularly useful in scenarios where evaluating each configuration is time-consuming or resource-intensive.

What potential challenges could arise from relying heavily on surrogate models in evolutionary algorithms

Relying heavily on surrogate models in evolutionary algorithms can introduce several challenges. One major challenge is ensuring the accuracy and reliability of the surrogate model. If the surrogate model does not accurately represent the true objective landscape, it may lead to suboptimal solutions or premature convergence. Additionally, surrogate models require careful calibration and validation to prevent biases or inaccuracies from affecting the optimization process. Over-reliance on a single surrogate model without proper monitoring and adjustment can hinder algorithm performance.

How might incorporating clustering techniques impact the scalability of multi-objective optimization problems

Incorporating clustering techniques into multi-objective optimization problems can impact scalability in several ways. Clustering allows for grouping similar solutions together, which can help reduce redundancy and improve diversity within populations. This enhanced diversity often leads to better exploration of the solution space, especially in high-dimensional or complex problem domains.
However, clustering may also introduce challenges related to scalability. As the number of solutions increases, traditional clustering algorithms may struggle to efficiently group large datasets due to computational complexity and memory constraints. Scaling clustering techniques for massive datasets requires advanced algorithms that are capable of handling big data efficiently while maintaining cluster quality and interpretability.