Rethinking Architecture Selection in Differentiable NAS

• URL: http://arxiv.org/abs/2108.04392v1
• Date: Tue, 10 Aug 2021 00:53:39 GMT
• Title: Rethinking Architecture Selection in Differentiable NAS
• Authors: Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh
• Abstract summary: Differentiable Neural Architecture Search is one of the most popular NAS methods for its search efficiency and simplicity. We propose an alternative perturbation-based architecture selection that directly measures each operation's influence on the supernet. We find that several failure modes of DARTS can be greatly alleviated with the proposed selection method.
• Score: 74.61723678821049
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Differentiable Neural Architecture Search is one of the most popular Neural Architecture Search (NAS) methods for its search efficiency and simplicity, accomplished by jointly optimizing the model weight and architecture parameters in a weight-sharing supernet via gradient-based algorithms. At the end of the search phase, the operations with the largest architecture parameters will be selected to form the final architecture, with the implicit assumption that the values of architecture parameters reflect the operation strength. While much has been discussed about the supernet's optimization, the architecture selection process has received little attention. We provide empirical and theoretical analysis to show that the magnitude of architecture parameters does not necessarily indicate how much the operation contributes to the supernet's performance. We propose an alternative perturbation-based architecture selection that directly measures each operation's influence on the supernet. We re-evaluate several differentiable NAS methods with the proposed architecture selection and find that it is able to extract significantly improved architectures from the underlying supernets consistently. Furthermore, we find that several failure modes of DARTS can be greatly alleviated with the proposed selection method, indicating that much of the poor generalization observed in DARTS can be attributed to the failure of magnitude-based architecture selection rather than entirely the optimization of its supernet.

Related papers

• OStr-DARTS: Differentiable Neural Architecture Search based on Operation Strength [70.76342136866413]
  Differentiable architecture search (DARTS) has emerged as a promising technique for effective neural architecture search. DARTS suffers from the well-known degeneration issue which can lead to deteriorating architectures. We propose a novel criterion based on operation strength that estimates the importance of an operation by its effect on the final loss.
  arXiv  Detail & Related papers  (2024-09-22T13:16:07Z)
• EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition [54.99121380536659]
  Eye movement biometrics have received increasing attention thanks to its high secure identification. Deep learning (DL) models have been recently successfully applied for eye movement recognition. DL architecture still is determined by human prior knowledge. We propose EM-DARTS, a hierarchical differentiable architecture search algorithm to automatically design the DL architecture for eye movement recognition.
  arXiv  Detail & Related papers  (2024-09-22T13:11:08Z)
• Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling [3.759936323189417]
  This study focuses on the architecture complexity-aware one-shot NAS that optimize the objective function composed of the weighted sum of two metrics. The proposed method is applied to the architecture search of convolutional neural networks on the CIAFR-10 and ImageNet datasets.
  arXiv  Detail & Related papers  (2022-07-21T07:06:03Z)
• 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)
• iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients [75.41173109807735]
  Differentiable ARchiTecture Search (DARTS) has recently become the mainstream of neural architecture search (NAS) We tackle the hypergradient computation in DARTS based on the implicit function theorem. We show that the architecture optimisation with the proposed method, named iDARTS, is expected to converge to a stationary point.
  arXiv  Detail & Related papers  (2021-06-21T00:44:11Z)
• Smooth Variational Graph Embeddings for Efficient Neural Architecture Search [41.62970837629573]
  We propose a two-sided variational graph autoencoder, which allows to smoothly encode and accurately reconstruct neural architectures from various search spaces. We evaluate the proposed approach on neural architectures defined by the ENAS approach, the NAS-Bench-101 and the NAS-Bench-201 search spaces.
  arXiv  Detail & Related papers  (2020-10-09T17:05:41Z)
• Adversarially Robust Neural Architectures [43.74185132684662]
  This paper aims to improve the adversarial robustness of the network from the architecture perspective with NAS framework. We explore the relationship among adversarial robustness, Lipschitz constant, and architecture parameters. Our algorithm empirically achieves the best performance among all the models under various attacks on different datasets.
  arXiv  Detail & Related papers  (2020-09-02T08:52:15Z)
• Stage-Wise Neural Architecture Search [65.03109178056937]
  Modern convolutional networks such as ResNet and NASNet have achieved state-of-the-art results in many computer vision applications. These networks consist of stages, which are sets of layers that operate on representations in the same resolution. It has been demonstrated that increasing the number of layers in each stage improves the prediction ability of the network. However, the resulting architecture becomes computationally expensive in terms of floating point operations, memory requirements and inference time.
  arXiv  Detail & Related papers  (2020-04-23T14:16:39Z)

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.