Augmented Data as an Auxiliary Plug-in Towards Categorization of Crowdsourced Heritage Data

• URL: http://arxiv.org/abs/2107.03852v1
• Date: Thu, 8 Jul 2021 14:09:39 GMT
• Title: Augmented Data as an Auxiliary Plug-in Towards Categorization of Crowdsourced Heritage Data
• Authors: Shashidhar Veerappa Kudari, Akshaykumar Gunari, Adarsh Jamadandi, Ramesh Ashok Tabib, Uma Mudenagudi
• Abstract summary: We propose a strategy to mitigate the problem of inefficient clustering performance by introducing data augmentation as an auxiliary plug-in. We train a variant of Convolutional Autoencoder (CAE) with augmented data to construct the initial feature space as a novel model for deep clustering.
• Score: 2.609784101826762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a strategy to mitigate the problem of inefficient clustering performance by introducing data augmentation as an auxiliary plug-in. Classical clustering techniques such as K-means, Gaussian mixture model and spectral clustering are central to many data-driven applications. However, recently unsupervised simultaneous feature learning and clustering using neural networks also known as Deep Embedded Clustering (DEC) has gained prominence. Pioneering works on deep feature clustering focus on defining relevant clustering loss function and choosing the right neural network for extracting features. A central problem in all these cases is data sparsity accompanied by high intra-class and low inter-class variance, which subsequently leads to poor clustering performance and erroneous candidate assignments. Towards this, we employ data augmentation techniques to improve the density of the clusters, thus improving the overall performance. We train a variant of Convolutional Autoencoder (CAE) with augmented data to construct the initial feature space as a novel model for deep clustering. We demonstrate the results of proposed strategy on crowdsourced Indian Heritage dataset. Extensive experiments show consistent improvements over existing works.

Related papers

• Self-Supervised Graph Embedding Clustering [70.36328717683297]
  K-means one-step dimensionality reduction clustering method has made some progress in addressing the curse of dimensionality in clustering tasks. We propose a unified framework that integrates manifold learning with K-means, resulting in the self-supervised graph embedding framework.
  arXiv  Detail & Related papers  (2024-09-24T08:59:51Z)
• Toward Efficient and Incremental Spectral Clustering via Parametric Spectral Clustering [2.44755919161855]
  Spectral clustering is a popular method for effectively clustering nonlinearly separable data. This paper introduces a novel approach called parametric spectral clustering (PSC) PSC addresses the challenges associated with big data and real-time scenarios.
  arXiv  Detail & Related papers  (2023-11-14T01:26:20Z)
• Reinforcement Graph Clustering with Unknown Cluster Number [91.4861135742095]
  We propose a new deep graph clustering method termed Reinforcement Graph Clustering. In our proposed method, cluster number determination and unsupervised representation learning are unified into a uniform framework. In order to conduct feedback actions, the clustering-oriented reward function is proposed to enhance the cohesion of the same clusters and separate the different clusters.
  arXiv  Detail & Related papers  (2023-08-13T18:12:28Z)
• Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation [65.268245109828]
  Online deep clustering refers to the joint use of a feature extraction network and a clustering model to assign cluster labels to each new data point or batch as it is processed. While faster and more versatile than offline methods, online clustering can easily reach the collapsed solution where the encoder maps all inputs to the same point and all are put into a single cluster. We propose a method that does not require data augmentation, and that, differently from existing methods, regularizes the hard assignments.
  arXiv  Detail & Related papers  (2023-03-29T08:23:26Z)
• DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks [53.88811980967342]
  This paper presents a Deep Clustering via Ensembles (DeepCluE) approach. It bridges the gap between deep clustering and ensemble clustering by harnessing the power of multiple layers in deep neural networks. Experimental results on six image datasets confirm the advantages of DeepCluE over the state-of-the-art deep clustering approaches.
  arXiv  Detail & Related papers  (2022-06-01T09:51:38Z)
• Deep clustering with fusion autoencoder [0.0]
  Deep clustering (DC) models capitalize on autoencoders to learn intrinsic features which facilitate the clustering process in consequence. In this paper, a novel DC method is proposed to address this issue. Specifically, the generative adversarial network and VAE are coalesced into a new autoencoder called fusion autoencoder (FAE)
  arXiv  Detail & Related papers  (2022-01-11T07:38:03Z)
• Deep Attention-guided Graph Clustering with Dual Self-supervision [49.040136530379094]
  We propose a novel method, namely deep attention-guided graph clustering with dual self-supervision (DAGC) We develop a dual self-supervision solution consisting of a soft self-supervision strategy with a triplet Kullback-Leibler divergence loss and a hard self-supervision strategy with a pseudo supervision loss. Our method consistently outperforms state-of-the-art methods on six benchmark datasets.
  arXiv  Detail & Related papers  (2021-11-10T06:53:03Z)
• Attention-driven Graph Clustering Network [49.040136530379094]
  We propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN) AGCN exploits a heterogeneous-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature. AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion.
  arXiv  Detail & Related papers  (2021-08-12T02:30:38Z)
• Very Compact Clusters with Structural Regularization via Similarity and Connectivity [3.779514860341336]
  We propose an end-to-end deep clustering algorithm, i.e., Very Compact Clusters (VCC) for the general datasets. Our proposed approach achieves better clustering performance over most of the state-of-the-art clustering methods.
  arXiv  Detail & Related papers  (2021-06-09T23:22:03Z)
• Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning [2.8028128734158164]
  Cluster assignment of large and complex images is a crucial but challenging task in pattern recognition and computer vision. We present a novel evolutionary unsupervised learning representation model with iterative optimization. We jointly fuzzy clustering to the deep reconstruction model, in which fuzzy membership is utilized to represent a clear structure of deep cluster assignments.
  arXiv  Detail & Related papers  (2021-03-31T13:58:10Z)
• Joint Optimization of an Autoencoder for Clustering and Embedding [22.16059261437617]
  We present an alternative where the autoencoder and the clustering are learned simultaneously. That simple neural network, referred to as the clustering module, can be integrated into a deep autoencoder resulting in a deep clustering model.
  arXiv  Detail & Related papers  (2020-12-07T14:38:10Z)

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.