DeepFRC: An End-to-End Deep Learning Model for Functional Registration and Classification

• URL: http://arxiv.org/abs/2501.18116v1
• Date: Thu, 30 Jan 2025 03:35:03 GMT
• Title: DeepFRC: An End-to-End Deep Learning Model for Functional Registration and Classification
• Authors: Siyuan Jiang, Yihan Hu, Wenjie Li, Pengcheng Zeng,
• Abstract summary: Functional data analysis (FDA) is essential for analyzing continuous, high-dimensional data.<n>We present DeepFRC, an end-to-end deep learning framework that unifies these tasks within a single model.<n> Experiments on real-world datasets demonstrate that DeepFRC consistently outperforms state-of-the-art methods.
• Score: 6.365405684671285
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Functional data analysis (FDA) is essential for analyzing continuous, high-dimensional data, yet existing methods often decouple functional registration and classification, limiting their efficiency and performance. We present DeepFRC, an end-to-end deep learning framework that unifies these tasks within a single model. Our approach incorporates an alignment module that learns time warping functions via elastic function registration and a learnable basis representation module for dimensionality reduction on aligned data. This integration enhances both alignment accuracy and predictive performance. Theoretical analysis establishes that DeepFRC achieves low misalignment and generalization error, while simulations elucidate the progression of registration, reconstruction, and classification during training. Experiments on real-world datasets demonstrate that DeepFRC consistently outperforms state-of-the-art methods, particularly in addressing complex registration challenges. Code is available at: https://github.com/Drivergo-93589/DeepFRC.

Related papers

• Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition [63.55828203989405]
  We introduce a novel Topology-Aware Modeling (TAM) framework for Sim2Real UDA on object point clouds.<n>Our approach mitigates the domain gap by leveraging global spatial topology, characterized by low-level, high-frequency 3D structures.<n>We propose an advanced self-training strategy that combines cross-domain contrastive learning with self-training.
  arXiv  Detail & Related papers  (2025-06-26T11:53:59Z)
• EKPC: Elastic Knowledge Preservation and Compensation for Class-Incremental Learning [53.88000987041739]
  Class-Incremental Learning (CIL) aims to enable AI models to continuously learn from sequentially arriving data of different classes over time.<n>We propose the Elastic Knowledge Preservation and Compensation (EKPC) method, integrating Importance-aware importance Regularization (IPR) and Trainable Semantic Drift Compensation (TSDC) for CIL.
  arXiv  Detail & Related papers  (2025-06-14T05:19:58Z)
• Binary Cumulative Encoding meets Time Series Forecasting [0.11704154007740832]
  We introduce binary cumulative encoding (BCE) that represents scalar targets into monotonic binary vectors.<n>BCE implicitly preserves order and magnitude information, allowing the model to learn distance-aware representations while still operating within a classification framework.<n>We show that our approach outperforms widely used methods in both point and probabilistic forecasting, while requiring fewer parameters and enabling faster training.
  arXiv  Detail & Related papers  (2025-05-30T13:41:39Z)
• SDF-TopoNet: A Two-Stage Framework for Tubular Structure Segmentation via SDF Pre-training and Topology-Aware Fine-Tuning [2.3436632098950456]
  Key challenge is ensuring topological correctness while maintaining computational efficiency. We propose textbfSDF-TopoNet, an improved topology-aware segmentation framework. We show that SDF-TopoNet outperforms existing methods in both topological accuracy and quantitative segmentation metrics.
  arXiv  Detail & Related papers  (2025-03-14T23:54:38Z)
• Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners [82.72552644267724]
  BoostPFN can outperform standard PFNs with the same size of training samples in large datasets. High performance is maintained for up to 50x of the pre-training size of PFNs.
  arXiv  Detail & Related papers  (2025-03-03T07:31:40Z)
• Locally Adaptive One-Class Classifier Fusion with Dynamic $\ell$p-Norm Constraints for Robust Anomaly Detection [17.93058599783703]
  We introduce a framework that dynamically adjusts fusion weights based on local data characteristics. Our method incorporates an interior-point optimization technique that significantly improves computational efficiency. The framework's ability to adapt to local data patterns while maintaining computational efficiency makes it particularly valuable for real-time applications.
  arXiv  Detail & Related papers  (2024-11-10T09:57:13Z)
• Simulation-Free Training of Neural ODEs on Paired Data [20.36333430055869]
  We employ the flow matching framework for simulation-free training of NODEs. We show that applying flow matching directly between paired data can often lead to an ill-defined flow. We propose a simple extension that applies flow matching in the embedding space of data pairs.
  arXiv  Detail & Related papers  (2024-10-30T11:18:27Z)
• TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction [10.765457133033435]
  This work introduces TemporalPaD, a novel end-to-end deep learning framework designed for temporal pattern datasets. The framework consists of three cooperative modules: a Policy Module, a Representation Module, and a Classification Module. We comprehensively evaluate TemporalPaD using 29 UCI datasets, a well-known benchmark for validating feature reduction algorithms.
  arXiv  Detail & Related papers  (2024-09-27T09:56:20Z)
• Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review [50.78587571704713]
  Learn-Focus-Review (LFR) is a dynamic training approach that adapts to the model's learning progress.<n>LFR tracks the model's learning performance across data blocks (sequences of tokens) and prioritizes revisiting challenging regions of the dataset.<n>Compared to baseline models trained on the full datasets, LFR consistently achieved lower perplexity and higher accuracy.
  arXiv  Detail & Related papers  (2024-09-10T00:59:18Z)
• A PAC-Bayesian Perspective on the Interpolating Information Criterion [54.548058449535155]
  We show how a PAC-Bayes bound is obtained for a general class of models, characterizing factors which influence performance in the interpolating regime. We quantify how the test error for overparameterized models achieving effectively zero training error depends on the quality of the implicit regularization imposed by e.g. the combination of model, parameter-initialization scheme.
  arXiv  Detail & Related papers  (2023-11-13T01:48:08Z)
• Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection [37.99031842449251]
  Video anomaly detection under weak supervision presents significant challenges. We present a weakly supervised anomaly detection framework that focuses on efficient context modeling and enhanced semantic discriminability. Our approach significantly improves the detection accuracy of certain anomaly sub-classes, underscoring its practical value and efficacy.
  arXiv  Detail & Related papers  (2023-06-26T06:45:16Z)
• ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
  We introduce Action-Aware Embodied Learning for Perception (ALP) ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. We show that ALP outperforms existing baselines in several downstream perception tasks.
  arXiv  Detail & Related papers  (2023-06-16T21:51:04Z)
• Robust Learning with Progressive Data Expansion Against Spurious Correlation [65.83104529677234]
  We study the learning process of a two-layer nonlinear convolutional neural network in the presence of spurious features. Our analysis suggests that imbalanced data groups and easily learnable spurious features can lead to the dominance of spurious features during the learning process. We propose a new training algorithm called PDE that efficiently enhances the model's robustness for a better worst-group performance.
  arXiv  Detail & Related papers  (2023-06-08T05:44:06Z)
• Distributed Gradient Descent for Functional Learning [9.81463654618448]
  We propose a novel distributed gradient descent functional learning (DGDFL) algorithm to tackle functional data across numerous local machines (processors) in the framework of reproducing kernel Hilbert space. Under mild conditions, confidence-based optimal learning rates of DGDFL are obtained without the saturation boundary on the regularity index suffered in previous works in functional regression.
  arXiv  Detail & Related papers  (2023-05-12T12:15:42Z)
• Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach [66.9033666087719]
  This paper extends the inference view and describes a variational inference formulation of federated learning. We apply FedEP on standard federated learning benchmarks and find that it outperforms strong baselines in terms of both convergence speed and accuracy.
  arXiv  Detail & Related papers  (2023-02-08T17:58:11Z)
• SCAI: A Spectral data Classification framework with Adaptive Inference for the IoT platform [0.0]
  We propose a Spectral data Classification framework with Adaptive Inference. Specifically, to allocate different computations for different samples while better exploiting the collaboration among different devices. To the best of our knowledge, this paper is the first attempt to conduct optimization by adaptive inference for spectral detection under the IoT platform.
  arXiv  Detail & Related papers  (2022-06-24T09:22:52Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)
• Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification [69.26747803963907]
  Rank-R Feedforward Neural Network (FNN) is a tensor-based nonlinear learning model that imposes Canonical/Polyadic decomposition on its parameters. First, it handles inputs as multilinear arrays, bypassing the need for vectorization, and can thus fully exploit the structural information along every data dimension. We establish the universal approximation and learnability properties of Rank-R FNN, and we validate its performance on real-world hyperspectral datasets.
  arXiv  Detail & Related papers  (2021-04-11T16:37:32Z)
• Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork [73.94896986868146]
  Phase retrieval is an important component in modern computational imaging systems. Recent advances in deep learning have opened up a new possibility for robust and fast PR. We develop a novel framework for deep unfolding to overcome the existing limitations.
  arXiv  Detail & Related papers  (2021-01-12T08:36:23Z)
• Progressive Self-Guided Loss for Salient Object Detection [102.35488902433896]
  We present a progressive self-guided loss function to facilitate deep learning-based salient object detection in images. Our framework takes advantage of adaptively aggregated multi-scale features to locate and detect salient objects effectively.
  arXiv  Detail & Related papers  (2021-01-07T07:33:38Z)
• Learning summary features of time series for likelihood free inference [93.08098361687722]
  We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
  arXiv  Detail & Related papers  (2020-12-04T19:21:37Z)
• Feature space approximation for kernel-based supervised learning [2.653409741248232]
  The goal is to reduce the size of the training data, resulting in lower storage consumption and computational complexity. We demonstrate significant improvements in comparison to the computation of data-driven predictions involving the full training data set. The method is applied to classification and regression problems from different application areas such as image recognition, system identification, and oceanographic time series analysis.
  arXiv  Detail & Related papers  (2020-11-25T11:23:58Z)

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.