Evolutionary Neural Architecture Search for 3D Point Cloud Analysis

• URL: http://arxiv.org/abs/2408.05556v1
• Date: Sat, 10 Aug 2024 13:41:18 GMT
• Title: Evolutionary Neural Architecture Search for 3D Point Cloud Analysis
• Authors: Yisheng Yang, Guodong Du, Chean Khim Toa, Ho-Kin Tang, Sim Kuan Goh,
• Abstract summary: This paper presents Success-History-based Self-adaptive Differential Evolution with a Joint Point Interaction Dimension Search (SHSADE-PIDS) SHSADE-PIDS is an evolutionary NAS framework that encodes discrete deep neural network architectures to continuous spaces and performs searches in the continuous spaces for efficient point cloud neural architectures. Comprehensive experiments on challenging 3D segmentation and classification benchmarks demonstrate SHSADE-PIDS's capabilities.
• Score: 0.6378824981027466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural architecture search (NAS) automates neural network design by using optimization algorithms to navigate architecture spaces, reducing the burden of manual architecture design. While NAS has achieved success, applying it to emerging domains, such as analyzing unstructured 3D point clouds, remains underexplored due to the data lying in non-Euclidean spaces, unlike images. This paper presents Success-History-based Self-adaptive Differential Evolution with a Joint Point Interaction Dimension Search (SHSADE-PIDS), an evolutionary NAS framework that encodes discrete deep neural network architectures to continuous spaces and performs searches in the continuous spaces for efficient point cloud neural architectures. Comprehensive experiments on challenging 3D segmentation and classification benchmarks demonstrate SHSADE-PIDS's capabilities. It discovered highly efficient architectures with higher accuracy, significantly advancing prior NAS techniques. For segmentation on SemanticKITTI, SHSADE-PIDS attained 64.51% mean IoU using only 0.55M parameters and 4.5GMACs, reducing overhead by over 22-26X versus other top methods. For ModelNet40 classification, it achieved 93.4% accuracy with just 1.31M parameters, surpassing larger models. SHSADE-PIDS provided valuable insights into bridging evolutionary algorithms with neural architecture optimization, particularly for emerging frontiers like point cloud learning.

Related papers

• Differentiable architecture search with multi-dimensional attention for spiking neural networks [4.318876451929319]
  Spiking Neural Networks (SNNs) have gained enormous popularity in the field of artificial intelligence. The majority of SNN methods directly inherit the structure of Artificial Neural Networks (ANN) We propose Multi-Attention Differentiable Architecture Search (MA-DARTS) to directly automate the search for the optimal network structure of SNNs.
  arXiv  Detail & Related papers  (2024-11-01T07:18:32Z)
• 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)
• GeNAS: Neural Architecture Search with Better Generalization [14.92869716323226]
  Recent neural architecture search (NAS) approaches rely on validation loss or accuracy to find the superior network for the target data. In this paper, we investigate a new neural architecture search measure for excavating architectures with better generalization.
  arXiv  Detail & Related papers  (2023-05-15T12:44:54Z)
• Neural Architecture Search for Speech Emotion Recognition [72.1966266171951]
  We propose to apply neural architecture search (NAS) techniques to automatically configure the SER models. We show that NAS can improve SER performance (54.89% to 56.28%) while maintaining model parameter sizes.
  arXiv  Detail & Related papers  (2022-03-31T10:16:10Z)
• 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)
• Search to aggregate neighborhood for graph neural network [47.47628113034479]
  We propose a framework, which tries to Search to Aggregate NEighborhood (SANE) to automatically design data-specific GNN architectures. By designing a novel and expressive search space, we propose a differentiable search algorithm, which is more efficient than previous reinforcement learning based methods.
  arXiv  Detail & Related papers  (2021-04-14T03:15:19Z)
• Differentiable Neural Architecture Learning for Efficient Neural Network Design [31.23038136038325]
  We introduce a novel emph architecture parameterisation based on scaled sigmoid function. We then propose a general emphiable Neural Architecture Learning (DNAL) method to optimize the neural architecture without the need to evaluate candidate neural networks.
  arXiv  Detail & Related papers  (2021-03-03T02:03:08Z)
• Neural Architecture Search of SPD Manifold Networks [79.45110063435617]
  We propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks. We first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design. We exploit a differentiable NAS algorithm on our relaxed continuous search space for SPD neural architecture search.
  arXiv  Detail & Related papers  (2020-10-27T18:08:57Z)
• Disentangled Neural Architecture Search [7.228790381070109]
  We propose disentangled neural architecture search (DNAS) which disentangles the hidden representation of the controller into semantically meaningful concepts. DNAS successfully disentangles the architecture representations, including operation selection, skip connections, and number of layers. Dense-sampling leads to neural architecture search with higher efficiency and better performance.
  arXiv  Detail & Related papers  (2020-09-24T03:35:41Z)
• DrNAS: Dirichlet Neural Architecture Search [88.56953713817545]
  We treat the continuously relaxed architecture mixing weight as random variables, modeled by Dirichlet distribution. With recently developed pathwise derivatives, the Dirichlet parameters can be easily optimized with gradient-based generalization. To alleviate the large memory consumption of differentiable NAS, we propose a simple yet effective progressive learning scheme.
  arXiv  Detail & Related papers  (2020-06-18T08:23:02Z)
• A Semi-Supervised Assessor of Neural Architectures [157.76189339451565]
  We employ an auto-encoder to discover meaningful representations of neural architectures. A graph convolutional neural network is introduced to predict the performance of architectures.
  arXiv  Detail & Related papers  (2020-05-14T09:02:33Z)

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.