Principled change point detection via representation learning

• URL: http://arxiv.org/abs/2106.02602v1
• Date: Fri, 4 Jun 2021 17:04:13 GMT
• Title: Principled change point detection via representation learning
• Authors: Evgenia Romanenkova and Alexey Zaytsev and Ramil Zainulin and Matvey Morozov
• Abstract summary: We introduce a principled differentiable loss function that considers the specificity of the CPD task. We propose an end-to-end method for the training of deep representation learning CPD models.
• Score: 0.6047855579999899
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Change points are abrupt alterations in the distribution of sequential data. A change-point detection (CPD) model aims at quick detection of such changes. Classic approaches perform poorly for semi-structured sequential data because of the absence of adequate data representation learning. To deal with it, we introduce a principled differentiable loss function that considers the specificity of the CPD task. The theoretical results suggest that this function approximates well classic rigorous solutions. For such loss function, we propose an end-to-end method for the training of deep representation learning CPD models. Our experiments provide evidence that the proposed approach improves baseline results of change point detection for various data types, including real-world videos and image sequences, and improve representations for them.

Related papers

• Influence Functions for Scalable Data Attribution in Diffusion Models [52.92223039302037]
  Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. In this paper, we aim to help address such challenges by developing an textitinfluence functions framework.
  arXiv  Detail & Related papers  (2024-10-17T17:59:02Z)
• PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings [55.55445978692678]
  PseudoNeg-MAE is a self-supervised learning framework that enhances global feature representation of point cloud mask autoencoders. We show that PseudoNeg-MAE achieves state-of-the-art performance on the ModelNet40 and ScanObjectNN datasets.
  arXiv  Detail & Related papers  (2024-09-24T07:57:21Z)
• Enhancing Changepoint Detection: Penalty Learning through Deep Learning Techniques [2.094821665776961]
  This study introduces a novel deep learning method for predicting penalty parameters. It leads to demonstrably improved changepoint detection accuracy on large benchmark supervised labeled datasets.
  arXiv  Detail & Related papers  (2024-08-01T18:10:05Z)
• Greedy online change point detection [0.0]
  Greedy Online Change Point Detection (GOCPD) is a computationally appealing method which finds change points by maximizing the probability of the data coming from the (temporal) concatenation of two independent models. We show that, for time series with a single change point, this objective is unimodal and thus CPD can be accelerated via ternary search with logarithmic complexity.
  arXiv  Detail & Related papers  (2023-08-14T08:59:59Z)
• Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection [7.262048441360132]
  Few-shot object detection is an emerging topic in the area of few-shot learning and object detection. We propose a training scheme that allows learning inductive biases that can boost few-shot detection. The proposed approach yields interpretable loss functions, as opposed to highly parametric and complex few-shot meta-models.
  arXiv  Detail & Related papers  (2023-04-24T15:14:16Z)
• Deep learning model solves change point detection for multiple change types [69.77452691994712]
  A change points detection aims to catch an abrupt disorder in data distribution. We propose an approach that works in the multiple-distributions scenario.
  arXiv  Detail & Related papers  (2022-04-15T09:44:21Z)
• Revisiting Consistency Regularization for Semi-Supervised Learning [80.28461584135967]
  We propose an improved consistency regularization framework by a simple yet effective technique, FeatDistLoss. Experimental results show that our model defines a new state of the art for various datasets and settings.
  arXiv  Detail & Related papers  (2021-12-10T20:46:13Z)
• Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection [55.888835686183995]
  We propose a neural network-based meta-learning method for supervised anomaly detection. We experimentally demonstrate that the proposed method achieves better performance than existing anomaly detection and few-shot learning methods.
  arXiv  Detail & Related papers  (2021-03-01T01:43:04Z)
• Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation [69.34035527763916]
  Change point detection (CPD) aims to locate abrupt property changes in time series data. Recent CPD methods demonstrated the potential of using deep learning techniques, but often lack the ability to identify more subtle changes in the autocorrelation statistics of the signal. We employ an autoencoder-based methodology with a novel loss function, through which the used autoencoders learn a partially time-invariant representation that is tailored for CPD.
  arXiv  Detail & Related papers  (2020-08-21T15:03:21Z)
• Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation [1.929039244357139]
  We show how existing algorithms can be generalized using various binary classification and regression models. The algorithms are tested on several synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2020-01-17T15:45:38Z)

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.