A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions (2006.02903v3)

Abstract: Deep learning has made breakthroughs and substantial in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is crucial to the feature representation of data and the final performance. However, the design of the neural architecture heavily relies on the researchers' prior knowledge and experience. And due to the limitations of human' inherent knowledge, it is difficult for people to jump out of their original thinking paradigm and design an optimal model. Therefore, an intuitive idea would be to reduce human intervention as much as possible and let the algorithm automatically design the neural architecture. Neural Architecture Search (NAS) is just such a revolutionary algorithm, and the related research work is complicated and rich. Therefore, a comprehensive and systematic survey on the NAS is essential. Previously related surveys have begun to classify existing work mainly based on the key components of NAS: search space, search strategy, and evaluation strategy. While this classification method is more intuitive, it is difficult for readers to grasp the challenges and the landmark work involved. Therefore, in this survey, we provide a new perspective: beginning with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms, and then providing solutions for subsequent related research work. Besides, we conduct a detailed and comprehensive analysis, comparison, and summary of these works. Finally, we provide some possible future research directions.

A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions

The paper "A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions" provides an exhaustive examination of Neural Architecture Search (NAS), focusing on the critical challenges and corresponding solutions encountered in the field. NAS, as a domain within machine learning, aims to automate the design of neural architectures, thereby reducing human intervention and leveraging algorithms to optimize neural network architectures for improved performance.

Key Challenges and Solutions

The paper identifies four major characteristics of early NAS work that present significant challenges:

1. Global Search Space: Early NAS efforts involved searching within a vast global space, attempting to optimize all architecture components simultaneously. To address this, the concept of a modular search space was introduced. This approach focuses on searching for smaller, repeatable neural units called cells or blocks, significantly reducing complexity and computational demand.
2. Discrete Search Strategy: Traditional methods treated neural architecture search as a discrete problem, limiting the use of optimization techniques such as gradient descent. The introduction of continuous search strategies, such as those used in DARTS, allowed for gradient-based optimization by relaxing discrete choices into continuous spaces, thus enabling more efficient searches.
3. Search from Scratch: Previous NAS approaches often initiated searches without leveraging existing high-performance architectures, increasing computational costs. Neural architecture recycling emerged as a solution, where existing architectures are optimized further, reusing model knowledge to expedite and enhance the search process.
4. Fully Trained Models: Training each candidate architecture to full convergence was standard, consuming significant resources. Incomplete training strategies, such as parameter sharing and early stopping, were developed to estimate model performance more quickly without full training, optimizing resource usage and search times.

Numerical Results and Bold Claims

The paper systematically compares existing NAS methodologies, highlighting that solutions such as parameter sharing and modular search spaces have led to substantial reductions in search times and resource requirements. For example, methods utilizing modular search spaces and continuous strategies have achieved state-of-the-art performance with significantly reduced computational resources over traditional techniques.

Implications and Future Directions

The survey underscores that despite robust advancements, NAS still faces the challenge of optimizing for tasks beyond image classification, including object detection and multi-modal tasks. Moreover, it highlights the necessity for standardized evaluation benchmarks and baselines to ensure fair comparisons across different NAS methodologies.

There is an anticipation of NAS expanding into areas demanding complex neural architecture design, such as model compression and task-specific networks for detection and segmentation. As the field progresses, integrating NAS with hyperparameter optimization could lead to more holistic approaches to model development.

Conclusion

The paper effectively captures the state of NAS research, offering valuable insights into the intricate challenges and innovative solutions that have shaped its trajectory. By enhancing the efficiency and capability of NAS techniques, the field stands poised for broader applications and deeper integration into various machine learning tasks. As such, the ongoing refinement of NAS solutions will be critical to its evolution and impact on automated machine learning.