Exploring the Loss Landscape in Neural Architecture Search (2005.02960v3)

Abstract: Neural architecture search (NAS) has seen a steep rise in interest over the last few years. Many algorithms for NAS consist of searching through a space of architectures by iteratively choosing an architecture, evaluating its performance by training it, and using all prior evaluations to come up with the next choice. The evaluation step is noisy - the final accuracy varies based on the random initialization of the weights. Prior work has focused on devising new search algorithms to handle this noise, rather than quantifying or understanding the level of noise in architecture evaluations. In this work, we show that (1) the simplest hill-climbing algorithm is a powerful baseline for NAS, and (2), when the noise in popular NAS benchmark datasets is reduced to a minimum, hill-climbing to outperforms many popular state-of-the-art algorithms. We further back up this observation by showing that the number of local minima is substantially reduced as the noise decreases, and by giving a theoretical characterization of the performance of local search in NAS. Based on our findings, for NAS research we suggest (1) using local search as a baseline, and (2) denoising the training pipeline when possible.

• The paper demonstrates that a basic hill-climbing algorithm serves as an effective baseline in NAS when noise is minimized.
• The study reveals that denoising NAS benchmarks smooths the loss landscape and significantly enhances local search performance.
• The analysis confirms that reducing noise decreases local minima, thereby simplifying the optimization process for neural architecture discovery.

Overview of Exploring the Loss Landscape in Neural Architecture Search

The paper "Exploring the Loss Landscape in Neural Architecture Search" presents significant insights into the field of Neural Architecture Search (NAS), a technique increasingly prominent in automating the design of optimal neural network architectures for specific datasets. The fundamental issue it addresses is the inherent noise in evaluating the performance of neural architectures—a problem that complicates the optimization process in NAS.

The authors focus on two main contributions. First, they advocate for the simplest hill-climbing algorithm as a potent baseline in NAS. Second, they propose that when the noise in popular NAS benchmark datasets—such as NASBench101, NASBench201, and NASBench301/DARTS—is minimized, the hill-climbing algorithm outperforms several contemporary state-of-the-art NAS algorithms. The authors' experimental analysis demonstrates that with reduced noise, not only does the performance of local search improve, but also the number of local minima in the loss landscape decreases, resulting in a smoother optimization path.

Key Findings

1. Local Search as a Viable Baseline:
   • The paper finds that contrary to previous assumptions, the hill-climbing algorithm can serve as a strong baseline for NAS. This challenges the view that complex architectures are necessary to navigate the noisy environment usually present in NAS tasks.
2. Impact of Denoising:
   • When the noise in training pipelines is minimized, local search algorithms, including basic hill-climbing, outperform contemporary NAS techniques. This underscores the importance of focusing on reducing evaluation noise rather than solely devising new search strategies.
3. Reduction in Local Minima:
   • The paper notes a substantial reduction in the number of local minima in the loss landscape as noise decreases. This finding is theoretically supported by an analysis that characterizes the performance of local search under various noise levels.
4. Experimental Validation:
   • Experiments conducted on NASBench datasets confirm that in denoised environments, local search, which can be implemented succinctly, rivals more sophisticated NAS algorithms.

Theoretical Insight and Implications

The paper contributes to the theoretical understanding of noise in NAS by providing a comprehensive characterization of local search performance. The authors propose a framework for evaluating NAS problems through a probabilistic graph optimization perspective, which yields predictions about the number of local minima and the ease of finding near-optimal solutions.

The practical implications of this paper are pivotal for improving NAS methods. By emphasizing the benefits of denoising training practices, the findings encourage broader adoption of noise reduction techniques such as regularization and adjustments to hyperparameters in the training pipeline. Moreover, this work suggests that a focus on reducing complexity and promoting simple local search methods could yield positive results in NAS research.

Future Directions

Future research could explore more sophisticated variants of local search, such as Tabu search or multi-fidelity approaches, to further validate the findings. Theoretically, extending the probabilistic framework to better model complex, non-uniform noise distributions could refine predictions about NAS performance and robustness.

In conclusion, this paper provides a nuanced understanding of the loss landscape in NAS and makes a strong case for the reconsideration of local search as a competitive approach in noisy environments. The insights presented will likely shape subsequent research into both the theoretical and practical aspects of NAS.