Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches

• URL: http://arxiv.org/abs/2007.03123v1
• Date: Mon, 6 Jul 2020 23:38:14 GMT
• Title: Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches
• Authors: Kalun Ho, Janis Keuper, Franz-Josef Pfreundt and Margret Keuper
• Abstract summary: We evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. We train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss. We propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods.
• Score: 10.42820615166362
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.

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)
• A3S: A General Active Clustering Method with Pairwise Constraints [66.74627463101837]
  A3S features strategic active clustering adjustment on the initial cluster result, which is obtained by an adaptive clustering algorithm. In extensive experiments across diverse real-world datasets, A3S achieves desired results with significantly fewer human queries.
  arXiv  Detail & Related papers  (2024-07-14T13:37:03Z)
• The Triad of Failure Modes and a Possible Way Out [7.977229957867868]
  We present a novel objective function for cluster-based self-supervised learning (SSL) that is designed to circumvent the triad of failure modes. This objective consists of three key components: (i) A generative term that penalizes representation collapse, (ii) a term that promotes invariance to data augmentations, and (ii) a uniformity term that penalizes cluster collapse.
  arXiv  Detail & Related papers  (2023-09-27T05:54:14Z)
• Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework [74.25493157757943]
  We formulate a novel clustering model, which exploits the non-negative feature property and incorporates the multi-view information into a unified joint learning framework. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features.
  arXiv  Detail & Related papers  (2022-11-03T08:18:27Z)
• The Group Loss++: A deeper look into group loss for deep metric learning [65.19665861268574]
  Group Loss is a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group. We show state-of-the-art results on clustering and image retrieval on four datasets, and present competitive results on two person re-identification datasets.
  arXiv  Detail & Related papers  (2022-04-04T14:09:58Z)
• Annotation Efficient Person Re-Identification with Diverse Cluster-Based Pair Selection [39.61651209527681]
  Person Re-identification (Re-ID) has attracted great attention due to its promising real-world applications. It is always costly to annotate the training data to train a Re-ID model, and it still remains challenging to reduce the annotation cost while maintaining the performance for the Re-ID task. We propose the Efficient Person Re-identification method to select image pairs from an alternative pair set according to the fallibility and diversity of pairs, and train the Re-ID model based on the annotation.
  arXiv  Detail & Related papers  (2022-03-10T14:37:07Z)
• Contrastive Fine-grained Class Clustering via Generative Adversarial Networks [9.667133604169829]
  We introduce C3-GAN, a method that leverages the categorical inference power of InfoGAN by applying contrastive learning. C3-GAN achieved state-of-the-art clustering performance on four fine-grained benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-30T08:57:11Z)
• Cluster Analysis with Deep Embeddings and Contrastive Learning [0.0]
  This work proposes a novel framework for performing image clustering from deep embeddings. Our approach jointly learns representations and predicts cluster centers in an end-to-end manner. Our framework performs on par with widely accepted clustering methods and outperforms the state-of-the-art contrastive learning method on the CIFAR-10 dataset.
  arXiv  Detail & Related papers  (2021-09-26T22:18:15Z)
• Clustering by Maximizing Mutual Information Across Views [62.21716612888669]
  We propose a novel framework for image clustering that incorporates joint representation learning and clustering. Our method significantly outperforms state-of-the-art single-stage clustering methods across a variety of image datasets.
  arXiv  Detail & Related papers  (2021-07-24T15:36:49Z)
• 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)
• A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints [5.639904484784126]
  We introduce a network framework for semi-supervised clustering with pairwise constraints. In contrast to existing approaches, we decompose SSC into two simpler classification tasks/stages. The proposed approach, S3C2, is motivated by the observation that binary classification is usually easier than multi-class clustering.
  arXiv  Detail & Related papers  (2020-01-18T20:13:07Z)

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.