Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation Importance

• URL: http://arxiv.org/abs/2303.16938v1
• Date: Wed, 29 Mar 2023 18:03:28 GMT
• Title: Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation Importance
• Authors: Vasco Lopes, Bruno Degardin, Lu\'is A. Alexandre
• Abstract summary: We conduct an empirical analysis of the widely used NAS-Bench-101, NAS-Bench-201 and TransNAS-Bench-101 benchmarks. We found that only a subset of the operation pool is required to generate architectures close to the upper-bound of the performance range. We consistently found convolution layers to have the highest impact on the architecture's performance.
• Score: 5.065947993017157
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural Architecture Search (NAS) benchmarks significantly improved the capability of developing and comparing NAS methods while at the same time drastically reduced the computational overhead by providing meta-information about thousands of trained neural networks. However, tabular benchmarks have several drawbacks that can hinder fair comparisons and provide unreliable results. These usually focus on providing a small pool of operations in heavily constrained search spaces -- usually cell-based neural networks with pre-defined outer-skeletons. In this work, we conducted an empirical analysis of the widely used NAS-Bench-101, NAS-Bench-201 and TransNAS-Bench-101 benchmarks in terms of their generability and how different operations influence the performance of the generated architectures. We found that only a subset of the operation pool is required to generate architectures close to the upper-bound of the performance range. Also, the performance distribution is negatively skewed, having a higher density of architectures in the upper-bound range. We consistently found convolution layers to have the highest impact on the architecture's performance, and that specific combination of operations favors top-scoring architectures. These findings shed insights on the correct evaluation and comparison of NAS methods using NAS benchmarks, showing that directly searching on NAS-Bench-201, ImageNet16-120 and TransNAS-Bench-101 produces more reliable results than searching only on CIFAR-10. Furthermore, with this work we provide suggestions for future benchmark evaluations and design. The code used to conduct the evaluations is available at https://github.com/VascoLopes/NAS-Benchmark-Evaluation.

Related papers

• Efficacy of Neural Prediction-Based Zero-Shot NAS [0.04096453902709291]
  We propose a novel approach for zero-shot Neural Architecture Search (NAS) using deep learning. Our method employs Fourier sum of sines encoding for convolutional kernels, enabling the construction of a computational feed-forward graph with a structure similar to the architecture under evaluation. Experimental results show that our approach surpasses previous methods using graph convolutional networks in terms of correlation on the NAS-Bench-201 dataset and exhibits a higher convergence rate.
  arXiv  Detail & Related papers  (2023-08-31T14:54:06Z)
• Generalization Properties of NAS under Activation and Skip Connection Search [66.8386847112332]
  We study the generalization properties of Neural Architecture Search (NAS) under a unifying framework. We derive the lower (and upper) bounds of the minimum eigenvalue of the Neural Tangent Kernel (NTK) under the (in)finite-width regime. We show how the derived results can guide NAS to select the top-performing architectures, even in the case without training.
  arXiv  Detail & Related papers  (2022-09-15T12:11:41Z)
• NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy [37.72015163462501]
  We present an in-depth analysis of popular NAS algorithms and performance prediction methods across 25 different combinations of search spaces and datasets. We introduce NAS-Bench-Suite, a comprehensive and collection of NAS benchmarks, accessible through a unified interface.
  arXiv  Detail & Related papers  (2022-01-31T18:02:09Z)
• BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule [95.56873042777316]
  Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost. This paper formulates the neural architecture search as a distribution learning problem through relaxing the architecture weights into Gaussian distributions. We demonstrate how the differentiable NAS benefits from Bayesian principles, enhancing exploration and improving stability.
  arXiv  Detail & Related papers  (2021-11-25T18:13:42Z)
• Weak NAS Predictors Are All You Need [91.11570424233709]
  Recent predictor-based NAS approaches attempt to solve the problem with two key steps: sampling some architecture-performance pairs and fitting a proxy accuracy predictor. We shift the paradigm from finding a complicated predictor that covers the whole architecture space to a set of weaker predictors that progressively move towards the high-performance sub-space. Our method costs fewer samples to find the top-performance architectures on NAS-Bench-101 and NAS-Bench-201, and it achieves the state-of-the-art ImageNet performance on the NASNet search space.
  arXiv  Detail & Related papers  (2021-02-21T01:58:43Z)
• Binarized Neural Architecture Search for Efficient Object Recognition [120.23378346337311]
  Binarized neural architecture search (BNAS) produces extremely compressed models to reduce huge computational cost on embedded devices for edge computing. An accuracy of $96.53%$ vs. $97.22%$ is achieved on the CIFAR-10 dataset, but with a significantly compressed model, and a $40%$ faster search than the state-of-the-art PC-DARTS.
  arXiv  Detail & Related papers  (2020-09-08T15:51:23Z)
• NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search [42.82951139084501]
  One-shot neural architecture search (NAS) has played a crucial role in making NAS methods computationally feasible in practice. We introduce a general framework for one-shot NAS that can be instantiated to many recently-introduced variants and introduce a general benchmarking framework.
  arXiv  Detail & Related papers  (2020-01-28T15:50:22Z)
• NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search [55.12928953187342]
  We propose an extension to NAS-Bench-101: NAS-Bench-201 with a different search space, results on multiple datasets, and more diagnostic information. NAS-Bench-201 has a fixed search space and provides a unified benchmark for almost any up-to-date NAS algorithms. We provide additional diagnostic information such as fine-grained loss and accuracy, which can give inspirations to new designs of NAS algorithms.
  arXiv  Detail & Related papers  (2020-01-02T05:28:26Z)
• NAS evaluation is frustratingly hard [1.7188280334580197]
  Neural Architecture Search (NAS) is an exciting new field which promises to be as much as a game-changer as Convolutional Neural Networks were in 2012. Comparison between different methods is still very much an open issue. Our first contribution is a benchmark of $8$ NAS methods on $5$ datasets.
  arXiv  Detail & Related papers  (2019-12-28T21:24:12Z)
• DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning [135.27931587381596]
  We propose an efficient and unified NAS framework termed DDPNAS via dynamic distribution pruning. In particular, we first sample architectures from a joint categorical distribution. Then the search space is dynamically pruned and its distribution is updated every few epochs. With the proposed efficient network generation method, we directly obtain the optimal neural architectures on given constraints.
  arXiv  Detail & Related papers  (2019-05-28T06:35:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.