Image Clustering Algorithm Based on Self-Supervised Pretrained Models and Latent Feature Distribution Optimization

• URL: http://arxiv.org/abs/2408.01920v2
• Date: Sat, 10 Aug 2024 06:14:36 GMT
• Title: Image Clustering Algorithm Based on Self-Supervised Pretrained Models and Latent Feature Distribution Optimization
• Authors: Qiuyu Zhu, Liheng Hu, Sijin Wang,
• Abstract summary: This paper introduces an image clustering algorithm based on self-supervised pretrained models and latent feature distribution optimization. Our approach outperforms the latest clustering algorithms and achieves state-of-the-art clustering results.
• Score: 4.39139858370436
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the face of complex natural images, existing deep clustering algorithms fall significantly short in terms of clustering accuracy when compared to supervised classification methods, making them less practical. This paper introduces an image clustering algorithm based on self-supervised pretrained models and latent feature distribution optimization, substantially enhancing clustering performance. It is found that: (1) For complex natural images, we effectively enhance the discriminative power of latent features by leveraging self-supervised pretrained models and their fine-tuning, resulting in improved clustering performance. (2) In the latent feature space, by searching for k-nearest neighbor images for each training sample and shortening the distance between the training sample and its nearest neighbor, the discriminative power of latent features can be further enhanced, and clustering performance can be improved. (3) In the latent feature space, reducing the distance between sample features and the nearest predefined cluster centroids can optimize the distribution of latent features, therefore further improving clustering performance. Through experiments on multiple datasets, our approach outperforms the latest clustering algorithms and achieves state-of-the-art clustering results. When the number of categories in the datasets is small, such as CIFAR-10 and STL-10, and there are significant differences between categories, our clustering algorithm has similar accuracy to supervised methods without using pretrained models, slightly lower than supervised methods using pre-trained models. The code linked algorithm is https://github.com/LihengHu/semi.

Related papers

• GCC: Generative Calibration Clustering [55.44944397168619]
  We propose a novel Generative Clustering (GCC) method to incorporate feature learning and augmentation into clustering procedure. First, we develop a discrimirative feature alignment mechanism to discover intrinsic relationship across real and generated samples. Second, we design a self-supervised metric learning to generate more reliable cluster assignment.
  arXiv  Detail & Related papers  (2024-04-14T01:51:11Z)
• Deep Embedding Clustering Driven by Sample Stability [16.53706617383543]
  We propose a deep embedding clustering algorithm driven by sample stability (DECS) Specifically, we start by constructing the initial feature space with an autoencoder and then learn the cluster-oriented embedding feature constrained by sample stability. The experimental results on five datasets illustrate that the proposed method achieves superior performance compared to state-of-the-art clustering approaches.
  arXiv  Detail & Related papers  (2024-01-29T09:19:49Z)
• A Weighted K-Center Algorithm for Data Subset Selection [70.49696246526199]
  Subset selection is a fundamental problem that can play a key role in identifying smaller portions of the training data. We develop a novel factor 3-approximation algorithm to compute subsets based on the weighted sum of both k-center and uncertainty sampling objective functions.
  arXiv  Detail & Related papers  (2023-12-17T04:41:07Z)
• Stable Cluster Discrimination for Deep Clustering [7.175082696240088]
  Deep clustering can optimize representations of instances (i.e., representation learning) and explore the inherent data distribution. The coupled objective implies a trivial solution that all instances collapse to the uniform features. In this work, we first show that the prevalent discrimination task in supervised learning is unstable for one-stage clustering. A novel stable cluster discrimination (SeCu) task is proposed and a new hardness-aware clustering criterion can be obtained accordingly.
  arXiv  Detail & Related papers  (2023-11-24T06:43:26Z)
• Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning [146.11600461034746]
  Method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervised algorithms to learn from unlabeled data. We prove that the core reason for this is lack of a clustering-friendly property in the embedding space.
  arXiv  Detail & Related papers  (2022-09-27T19:04:36Z)
• Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clustering [0.8543753708890495]
  In this paper, we realize mixture model-based clustering with a neural network. We train the network end-to-end via batch-wise EM iterations where the forward pass acts as the E-step and the backward pass acts as the M-step. Our trained networks outperform single-stage deep clustering methods that still depend on k-means.
  arXiv  Detail & Related papers  (2021-07-06T08:00:58Z)
• Learning the Precise Feature for Cluster Assignment [39.320210567860485]
  We propose a framework which integrates representation learning and clustering into a single pipeline for the first time. The proposed framework exploits the powerful ability of recently developed generative models for learning intrinsic features. Experimental results show that the performance of the proposed method is superior, or at least comparable to, the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-11T04:08:54Z)
• 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)
• Graph Contrastive Clustering [131.67881457114316]
  We propose a novel graph contrastive learning framework, which is then applied to the clustering task and we come up with the Graph Constrastive Clustering(GCC) method. Specifically, on the one hand, the graph Laplacian based contrastive loss is proposed to learn more discriminative and clustering-friendly features. On the other hand, a novel graph-based contrastive learning strategy is proposed to learn more compact clustering assignments.
  arXiv  Detail & Related papers  (2021-04-03T15:32:49Z)
• Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
  Existing scalable hierarchical clustering methods sacrifice quality for speed. We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
  arXiv  Detail & Related papers  (2020-10-22T15:58:35Z)
• Contrastive Clustering [57.71729650297379]
  We propose Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. In particular, CC achieves an NMI of 0.705 (0.431) on the CIFAR-10 (CIFAR-100) dataset, which is an up to 19% (39%) performance improvement compared with the best baseline.
  arXiv  Detail & Related papers  (2020-09-21T08:54:40Z)

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.