Related papers: SiamMM: A Mixture Model Perspective on Deep Unsupervised Learning

SiamMM: A Mixture Model Perspective on Deep Unsupervised Learning

URL: http://arxiv.org/abs/2511.05462v1
Date: Fri, 07 Nov 2025 18:07:42 GMT
Title: SiamMM: A Mixture Model Perspective on Deep Unsupervised Learning
Authors: Xiaodong Wang, Jing Huang, Kevin J Liang,
Abstract summary: In this work, we establish connections between unsupervised clustering methods and classical mixture models from statistics.<n>Our method attains state-of-the-art performance across various self-supervised learning benchmarks.<n> Inspection of the learned clusters reveals a strong resemblance to unseen ground truth labels, uncovering potential instances of mislabeling.
Score: 12.136107883911615
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Recent studies have demonstrated the effectiveness of clustering-based approaches for self-supervised and unsupervised learning. However, the application of clustering is often heuristic, and the optimal methodology remains unclear. In this work, we establish connections between these unsupervised clustering methods and classical mixture models from statistics. Through this framework, we demonstrate significant enhancements to these clustering methods, leading to the development of a novel model named SiamMM. Our method attains state-of-the-art performance across various self-supervised learning benchmarks. Inspection of the learned clusters reveals a strong resemblance to unseen ground truth labels, uncovering potential instances of mislabeling.

Related papers

Unsupervised Cognition [2.5069344340760713]
We propose a primitive-based, unsupervised learning approach for decision-making inspired by a novel cognition framework.<n>This representation-centric approach models the input space constructively as a distributed hierarchical structure in an input-agnostic way.
arXiv Detail & Related papers (2024-09-27T10:50:49Z)
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)
Class-Incremental Mixture of Gaussians for Deep Continual Learning [15.49323098362628]
We propose end-to-end incorporation of the mixture of Gaussians model into the continual learning framework. We show that our model can effectively learn in memory-free scenarios with fixed extractors.
arXiv Detail & Related papers (2023-07-09T04:33:19Z)
Semi-supervised learning made simple with self-supervised clustering [65.98152950607707]
Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations. We propose a conceptually simple yet empirically powerful approach to turn clustering-based self-supervised methods into semi-supervised learners.
arXiv Detail & Related papers (2023-06-13T01:09:18Z)
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors [71.79984112148865]
We introduce a method for ranking generative models based on the training dynamics exhibited during learning. Inspired by recent theoretical characterizations of disentanglement, our method does not require supervision of the underlying latent factors.
arXiv Detail & Related papers (2022-10-04T17:27:29Z)
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)
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id [109.1730454118532]
Unsupervised person re-identification (Re-Id) has attracted increasing attention due to its practical application in the read-world video surveillance system. We present the hybrid dynamic cluster contrast and probability distillation algorithm. It formulates the unsupervised Re-Id problem into an unified local-to-global dynamic contrastive learning and self-supervised probability distillation framework.
arXiv Detail & Related papers (2021-09-29T02:56:45Z)
Solving Inefficiency of Self-supervised Representation Learning [87.30876679780532]
Existing contrastive learning methods suffer from very low learning efficiency. Under-clustering and over-clustering problems are major obstacles to learning efficiency. We propose a novel self-supervised learning framework using a median triplet loss.
arXiv Detail & Related papers (2021-04-18T07:47:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.