Top-Down Deep Clustering with Multi-generator GANs

• URL: http://arxiv.org/abs/2112.03398v1
• Date: Mon, 6 Dec 2021 22:53:12 GMT
• Title: Top-Down Deep Clustering with Multi-generator GANs
• Authors: Daniel de Mello, Renato Assun\c{c}\~ao, Fabricio Murai
• Abstract summary: Deep clustering (DC) learns embedding spaces that are optimal for cluster analysis. We propose HC-MGAN, a new technique based on GANs with multiple generators (MGANs) Our method is inspired by the observation that each generator of a MGAN tends to generate data that correlates with a sub-region of the real data distribution.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep clustering (DC) leverages the representation power of deep architectures to learn embedding spaces that are optimal for cluster analysis. This approach filters out low-level information irrelevant for clustering and has proven remarkably successful for high dimensional data spaces. Some DC methods employ Generative Adversarial Networks (GANs), motivated by the powerful latent representations these models are able to learn implicitly. In this work, we propose HC-MGAN, a new technique based on GANs with multiple generators (MGANs), which have not been explored for clustering. Our method is inspired by the observation that each generator of a MGAN tends to generate data that correlates with a sub-region of the real data distribution. We use this clustered generation to train a classifier for inferring from which generator a given image came from, thus providing a semantically meaningful clustering for the real distribution. Additionally, we design our method so that it is performed in a top-down hierarchical clustering tree, thus proposing the first hierarchical DC method, to the best of our knowledge. We conduct several experiments to evaluate the proposed method against recent DC methods, obtaining competitive results. Last, we perform an exploratory analysis of the hierarchical clustering tree that highlights how accurately it organizes the data in a hierarchy of semantically coherent patterns.

Related papers

• Hierarchical Clustering for Conditional Diffusion in Image Generation [12.618079575423868]
  This paper introduces TreeDiffusion, a deep generative model that conditions Diffusion Models on hierarchical clusters to obtain high-quality, cluster-specific generations. The proposed pipeline consists of two steps: a VAE-based clustering model that learns the hierarchical structure of the data, and a conditional diffusion model that generates realistic images for each cluster.
  arXiv  Detail & Related papers  (2024-10-22T11:35:36Z)
• Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances [24.142013877384603]
  This paper introduces a novel unsupervised multimodal clustering method (UMC), making a pioneering contribution to this field. UMC introduces a unique approach to constructing augmentation views for multimodal data, which are then used to perform pre-training. We show remarkable improvements of 2-6% scores in clustering metrics over state-of-the-art methods, marking the first successful endeavor in this domain.
  arXiv  Detail & Related papers  (2024-05-21T13:24:07Z)
• 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)
• 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)
• 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)
• 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)
• EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs [0.0]
  Semi-supervised learning has been gaining attention as it allows for performing image analysis tasks such as classification with limited labeled data. Some popular algorithms using Generative Adrial Networks (GANs) for semi-supervised classification share a single architecture for classification and discrimination. This may require a model to converge to a separate data distribution for each task, which may reduce overall performance. We propose a novel GAN model namely External GAN (ECGAN) that utilizes GANs and semi-supervised algorithms to improve classification in fully-supervised tasks.
  arXiv  Detail & Related papers  (2020-12-26T05:58:00Z)
• 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)
• Structured Graph Learning for Clustering and Semi-supervised Classification [74.35376212789132]
  We propose a graph learning framework to preserve both the local and global structure of data. Our method uses the self-expressiveness of samples to capture the global structure and adaptive neighbor approach to respect the local structure. Our model is equivalent to a combination of kernel k-means and k-means methods under certain condition.
  arXiv  Detail & Related papers  (2020-08-31T08:41:20Z)
• Improving Generative Adversarial Networks with Local Coordinate Coding [150.24880482480455]
  Generative adversarial networks (GANs) have shown remarkable success in generating realistic data from some predefined prior distribution. In practice, semantic information might be represented by some latent distribution learned from data. We propose an LCCGAN model with local coordinate coding (LCC) to improve the performance of generating data.
  arXiv  Detail & Related papers  (2020-07-28T09:17:50Z)

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.