Bridging Distribution Learning and Image Clustering in High-dimensional Space

• URL: http://arxiv.org/abs/2308.15667v1
• Date: Tue, 29 Aug 2023 23:35:36 GMT
• Title: Bridging Distribution Learning and Image Clustering in High-dimensional Space
• Authors: Guanfang Dong, Chenqiu Zhao, Anup Basu
• Abstract summary: Distribution learning focuses on learning the probability density function from a set of data samples. clustering aims to group similar objects together in an unsupervised manner. In this paper, we use an autoencoder to encode images into a high-dimensional latent space.
• Score: 9.131712404284876
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Distribution learning focuses on learning the probability density function from a set of data samples. In contrast, clustering aims to group similar objects together in an unsupervised manner. Usually, these two tasks are considered unrelated. However, the relationship between the two may be indirectly correlated, with Gaussian Mixture Models (GMM) acting as a bridge. In this paper, we focus on exploring the correlation between distribution learning and clustering, with the motivation to fill the gap between these two fields, utilizing an autoencoder (AE) to encode images into a high-dimensional latent space. Then, Monte-Carlo Marginalization (MCMarg) and Kullback-Leibler (KL) divergence loss are used to fit the Gaussian components of the GMM and learn the data distribution. Finally, image clustering is achieved through each Gaussian component of GMM. Yet, the "curse of dimensionality" poses severe challenges for most clustering algorithms. Compared with the classic Expectation-Maximization (EM) Algorithm, experimental results show that MCMarg and KL divergence can greatly alleviate the difficulty. Based on the experimental results, we believe distribution learning can exploit the potential of GMM in image clustering within high-dimensional space.

Related papers

• Deep Gaussian mixture model for unsupervised image segmentation [1.3654846342364308]
  In many tasks sufficient pixel-level labels are very difficult to obtain. We propose a method which combines a Gaussian mixture model (GMM) with unsupervised deep learning techniques. We demonstrate the advantages of our method in various experiments on the example of infarct segmentation on multi-sequence MRI images.
  arXiv  Detail & Related papers  (2024-04-18T15:20:59Z)
• RGM: A Robust Generalizable Matching Model [49.60975442871967]
  We propose a deep model for sparse and dense matching, termed RGM (Robust Generalist Matching) To narrow the gap between synthetic training samples and real-world scenarios, we build a new, large-scale dataset with sparse correspondence ground truth. We are able to mix up various dense and sparse matching datasets, significantly improving the training diversity.
  arXiv  Detail & Related papers  (2023-10-18T07:30:08Z)
• Cramer Type Distances for Learning Gaussian Mixture Models by Gradient Descent [0.0]
  As of today, few known algorithms can fit or learn Gaussian mixture models. We propose a distance function called Sliced Cram'er 2-distance for learning general multivariate GMMs. These features are especially useful for distributional reinforcement learning and Deep Q Networks.
  arXiv  Detail & Related papers  (2023-07-13T13:43:02Z)
• Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID [56.573905143954015]
  We propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters. Under such a supervisory signal, a Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework is proposed to align features jointly at a cluster-level. Experiments on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2023-05-22T03:27:46Z)
• Compound Batch Normalization for Long-tailed Image Classification [77.42829178064807]
  We propose a compound batch normalization method based on a Gaussian mixture. It can model the feature space more comprehensively and reduce the dominance of head classes. The proposed method outperforms existing methods on long-tailed image classification.
  arXiv  Detail & Related papers  (2022-12-02T07:31:39Z)
• A new perspective on probabilistic image modeling [92.89846887298852]
  We present a new probabilistic approach for image modeling capable of density estimation, sampling and tractable inference. DCGMMs can be trained end-to-end by SGD from random initial conditions, much like CNNs. We show that DCGMMs compare favorably to several recent PC and SPN models in terms of inference, classification and sampling.
  arXiv  Detail & Related papers  (2022-03-21T14:53:57Z)
• Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources [10.445522754737496]
  This paper develops two algorithms to achieve federated generalized linear mixed effect models (GLMM) The log-likelihood function of GLMM is approximated by two numerical methods, which supports federated decomposition of GLMM to bring to data. Experiment results demonstrate comparable (Laplace) and superior (Gaussian-Hermite) performances with simulated and real-world data.
  arXiv  Detail & Related papers  (2021-09-28T21:01:30Z)
• Image Modeling with Deep Convolutional Gaussian Mixture Models [79.0660895390689]
  We present a new formulation of deep hierarchical Gaussian Mixture Models (GMMs) that is suitable for describing and generating images. DCGMMs avoid this by a stacked architecture of multiple GMM layers, linked by convolution and pooling operations. For generating sharp images with DCGMMs, we introduce a new gradient-based technique for sampling through non-invertible operations like convolution and pooling. Based on the MNIST and FashionMNIST datasets, we validate the DCGMMs model by demonstrating its superiority over flat GMMs for clustering, sampling and outlier detection.
  arXiv  Detail & Related papers  (2021-04-19T12:08:53Z)
• Kernel learning approaches for summarising and combining posterior similarity matrices [68.8204255655161]
  We build upon the notion of the posterior similarity matrix (PSM) in order to suggest new approaches for summarising the output of MCMC algorithms for Bayesian clustering models. A key contribution of our work is the observation that PSMs are positive semi-definite, and hence can be used to define probabilistically-motivated kernel matrices.
  arXiv  Detail & Related papers  (2020-09-27T14:16:14Z)
• An unsupervised deep learning framework via integrated optimization of representation learning and GMM-based modeling [31.334196673143257]
  This paper introduces a new principle of joint learning on both deep representations and GMM-based deep modeling. In comparison with the existing work in similar areas, our objective function has two learning targets, which are created to be jointly optimized. The compactness of clusters is significantly enhanced by reducing the intra-cluster distances, and the separability is improved by increasing the inter-cluster distances.
  arXiv  Detail & Related papers  (2020-09-11T04:57:03Z)
• GATCluster: Self-Supervised Gaussian-Attention Network for Image Clustering [9.722607434532883]
  We propose a self-supervised clustering network for image Clustering (GATCluster) Rather than extracting intermediate features first and then performing the traditional clustering, GATCluster semantic cluster labels without further post-processing. We develop a two-step learning algorithm that is memory-efficient for clustering large-size images.
  arXiv  Detail & Related papers  (2020-02-27T00:57:18Z)

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.