Effective, Efficient and Robust Neural Architecture Search

• URL: http://arxiv.org/abs/2011.09820v1
• Date: Thu, 19 Nov 2020 13:46:23 GMT
• Title: Effective, Efficient and Robust Neural Architecture Search
• Authors: Zhixiong Yue, Baijiong Lin, Xiaonan Huang, Yu Zhang
• Abstract summary: Recent advances in adversarial attacks show the vulnerability of deep neural networks searched by Neural Architecture Search (NAS) We propose an Effective, Efficient, and Robust Neural Architecture Search (E2RNAS) method to search a neural network architecture by taking the performance, robustness, and resource constraint into consideration. Experiments on benchmark datasets show that the proposed E2RNAS method can find adversarially robust architectures with optimized model size and comparable classification accuracy.
• Score: 4.273005643715522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in adversarial attacks show the vulnerability of deep neural networks searched by Neural Architecture Search (NAS). Although NAS methods can find network architectures with the state-of-the-art performance, the adversarial robustness and resource constraint are often ignored in NAS. To solve this problem, we propose an Effective, Efficient, and Robust Neural Architecture Search (E2RNAS) method to search a neural network architecture by taking the performance, robustness, and resource constraint into consideration. The objective function of the proposed E2RNAS method is formulated as a bi-level multi-objective optimization problem with the upper-level problem as a multi-objective optimization problem, which is different from existing NAS methods. To solve the proposed objective function, we integrate the multiple-gradient descent algorithm, a widely studied gradient-based multi-objective optimization algorithm, with the bi-level optimization. Experiments on benchmark datasets show that the proposed E2RNAS method can find adversarially robust architectures with optimized model size and comparable classification accuracy.

Related papers

• A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population Mechanism [58.855741970337675]
  Neural architecture search (NAS) enables re-searchers to automatically explore vast search spaces and find efficient neural networks. NAS suffers from a key bottleneck, i.e., numerous architectures need to be evaluated during the search process. We propose the SMEM-NAS, a pairwise com-parison relation-assisted multi-objective evolutionary algorithm based on a multi-population mechanism.
  arXiv  Detail & Related papers  (2024-07-22T12:46:22Z)
• DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs [53.82853297675979]
  1-bit convolutional neural networks (CNNs) with binary weights and activations show their potential for resource-limited embedded devices. One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS. We introduce Discrepant Child-Parent Neural Architecture Search (DCP-NAS) to efficiently search 1-bit CNNs.
  arXiv  Detail & Related papers  (2023-06-27T11:28:29Z)
• Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations [59.683234126055694]
  Recent neural architecture search (NAS) frameworks have been successful in finding optimal architectures for given conditions. We propose a novel lightweight robust zero-cost proxy that considers the consistency across features, parameters, and gradients of both clean and perturbed images. Our approach facilitates an efficient and rapid search for neural architectures capable of learning generalizable features that exhibit robustness across diverse perturbations.
  arXiv  Detail & Related papers  (2023-06-08T08:34:26Z)
• NAS-FCOS: Efficient Search for Object Detection Architectures [113.47766862146389]
  We propose an efficient method to obtain better object detectors by searching for the feature pyramid network (FPN) and the prediction head of a simple anchor-free object detector. With carefully designed search space, search algorithms, and strategies for evaluating network quality, we are able to find top-performing detection architectures within 4 days using 8 V100 GPUs.
  arXiv  Detail & Related papers  (2021-10-24T12:20:04Z)
• Going Beyond Neural Architecture Search with Sampling-based Neural Ensemble Search [31.059040393415003]
  We present two novel sampling algorithms under our Neural Ensemble Search via Sampling (NESS) framework. Our NESS algorithms are shown to be able to achieve improved performance in both classification and adversarial defense tasks.
  arXiv  Detail & Related papers  (2021-09-06T15:18:37Z)
• 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)
• Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery [54.60327265077322]
  We study two important problems in the automated design of neural networks through the lens of sparse recovery methods. In the first part of this paper, we establish a novel connection between HPO and structured sparse recovery. In the second part of this paper, we establish a connection between NAS and structured sparse recovery.
  arXiv  Detail & Related papers  (2020-07-07T00:57:09Z)
• Geometry-Aware Gradient Algorithms for Neural Architecture Search [41.943045315986744]
  We argue for the study of single-level empirical risk minimization to understand NAS with weight-sharing. We present a geometry-aware framework that exploits the underlying structure of this optimization to return sparse architectural parameters. We achieve state-of-the-art accuracy on the latest NAS benchmarks in computer vision.
  arXiv  Detail & Related papers  (2020-04-16T17:46:39Z)
• Neural Architecture Generator Optimization [9.082931889304723]
  We are first to investigate casting NAS as a problem of finding the optimal network generator. We propose a new, hierarchical and graph-based search space capable of representing an extremely large variety of network types.
  arXiv  Detail & Related papers  (2020-04-03T06:38:07Z)

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.