Learning Soft Sparse Shapes for Efficient Time-Series Classification

• URL: http://arxiv.org/abs/2505.06892v2
• Date: Tue, 03 Jun 2025 09:44:58 GMT
• Title: Learning Soft Sparse Shapes for Efficient Time-Series Classification
• Authors: Zhen Liu, Yicheng Luo, Boyuan Li, Emadeldeen Eldele, Min Wu, Qianli Ma,
• Abstract summary: We propose a Soft sparse Shapes (SoftShape) model for efficient time series classification.<n>Our approach mainly introduces soft shape sparsification and soft shape learning blocks.<n>The latter facilitates intra- and inter-shape temporal pattern learning, improving model efficiency by using sparsified soft shapes as inputs.
• Score: 25.128114360717454
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Shapelets are discriminative subsequences (or shapes) with high interpretability in time series classification. Due to the time-intensive nature of shapelet discovery, existing shapelet-based methods mainly focus on selecting discriminative shapes while discarding others to achieve candidate subsequence sparsification. However, this approach may exclude beneficial shapes and overlook the varying contributions of shapelets to classification performance. To this end, we propose a Soft sparse Shapes (SoftShape) model for efficient time series classification. Our approach mainly introduces soft shape sparsification and soft shape learning blocks. The former transforms shapes into soft representations based on classification contribution scores, merging lower-scored ones into a single shape to retain and differentiate all subsequence information. The latter facilitates intra- and inter-shape temporal pattern learning, improving model efficiency by using sparsified soft shapes as inputs. Specifically, we employ a learnable router to activate a subset of class-specific expert networks for intra-shape pattern learning. Meanwhile, a shared expert network learns inter-shape patterns by converting sparsified shapes into sequences. Extensive experiments show that SoftShape outperforms state-of-the-art methods and produces interpretable results.

Related papers

• Castor: Competing shapelets for fast and accurate time series classification [0.9208007322096533]
  Castor is a simple, efficient, and accurate time series classification algorithm. We show that Castor yields transformations that are significantly more accurate than several state-of-the-art classifiers.
  arXiv  Detail & Related papers  (2024-03-19T22:05:32Z)
• 3D Shape Completion on Unseen Categories:A Weakly-supervised Approach [61.76304400106871]
  We introduce a novel weakly-supervised framework to reconstruct the complete shapes from unseen categories. We first propose an end-to-end prior-assisted shape learning network that leverages data from the seen categories to infer a coarse shape. In addition, we propose a self-supervised shape refinement model to further refine the coarse shape.
  arXiv  Detail & Related papers  (2024-01-19T09:41:09Z)
• G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors [52.646396621449]
  G-MSM is a novel unsupervised learning approach for non-rigid shape correspondence. We construct an affinity graph on a given set of training shapes in a self-supervised manner. We demonstrate state-of-the-art performance on several recent shape correspondence benchmarks.
  arXiv  Detail & Related papers  (2022-12-06T12:09:24Z)
• Unsupervised Deep Multi-Shape Matching [15.050801537501462]
  3D shape matching is a long-standing problem in computer vision and computer graphics. We present a novel approach for deep multi-shape matching that ensures cycle-consistent multi-matchings. Our method achieves state-of-the-art results on several challenging benchmark datasets.
  arXiv  Detail & Related papers  (2022-07-20T01:22:08Z)
• Improving Shape Awareness and Interpretability in Deep Networks Using Geometric Moments [0.0]
  Deep networks for image classification often rely more on texture information than object shape. This paper presents a deep-learning model inspired by geometric moments. We demonstrate the effectiveness of our method on standard image classification datasets.
  arXiv  Detail & Related papers  (2022-05-24T02:08:05Z)
• Implicit field supervision for robust non-rigid shape matching [29.7672368261038]
  Establishing a correspondence between two non-rigidly deforming shapes is one of the most fundamental problems in visual computing. We introduce an approach based on auto-decoder framework, that learns a continuous shape-wise deformation field over a fixed template. Our method is remarkably robust in the presence of strong artefacts and can be generalised to arbitrary shape categories.
  arXiv  Detail & Related papers  (2022-03-15T07:22:52Z)
• Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transport [80.64516377977183]
  Shape matching has been a long-studied problem for the computer graphics and vision community. We investigate a hierarchical learning design, to which we incorporate local patch-level information and global shape-level structures. We propose a novel optimal transport solver by recurrently updating features on non-confident nodes to learn globally consistent correspondences between the shapes.
  arXiv  Detail & Related papers  (2022-02-03T11:41:46Z)
• Learning Debiased and Disentangled Representations for Semantic Segmentation [52.35766945827972]
  We propose a model-agnostic and training scheme for semantic segmentation. By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes. Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2021-10-31T16:15:09Z)
• Convolutional Shapelet Transform: A new approach for time series shapelets [1.160208922584163]
  We present a new formulation of time series shapelets including the notion of dilation, and a shapelet extraction method based on convolutional kernels. We show that our method improves on the state-of-the-art for shapelet algorithms, and we show that it can be used to interpret results from convolutional kernels.
  arXiv  Detail & Related papers  (2021-09-28T06:30:42Z)
• Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification [114.56752624945142]
  We argue that the most popular random sampling method, the well-known PK sampler, is not informative and efficient for deep metric learning. We propose an efficient mini batch sampling method called Graph Sampling (GS) for large-scale metric learning.
  arXiv  Detail & Related papers  (2021-04-04T06:44:15Z)
• Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapes [47.49826669394906]
  We propose an unsupervised learning framework for finding dense correspondences between point cloud shapes. In order to learn discriminative pointwise features from point cloud data, we incorporate in the formulation a regularization term based on Sinkhorn normalization. We show that the learned pointwise features can be leveraged by supervised methods to improve the part segmentation performance.
  arXiv  Detail & Related papers  (2020-07-19T05:21:33Z)
• Neural Subdivision [58.97214948753937]
  This paper introduces Neural Subdivision, a novel framework for data-driven coarseto-fine geometry modeling. We optimize for the same set of network weights across all local mesh patches, thus providing an architecture that is not constrained to a specific input mesh, fixed genus, or category. We demonstrate that even when trained on a single high-resolution mesh our method generates reasonable subdivisions for novel shapes.
  arXiv  Detail & Related papers  (2020-05-04T20:03:21Z)
• Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence [31.840880075039944]
  We present a novel learning-based approach for computing correspondences between non-rigid 3D shapes. Key to our method is a feature-extraction network that learns directly from raw shape geometry.
  arXiv  Detail & Related papers  (2020-03-31T15:20:17Z)

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.