Exploring the Limits of Deep Image Clustering using Pretrained Models (2303.17896v2)

Abstract: We present a general methodology that learns to classify images without labels by leveraging pretrained feature extractors. Our approach involves self-distillation training of clustering heads based on the fact that nearest neighbours in the pretrained feature space are likely to share the same label. We propose a novel objective that learns associations between image features by introducing a variant of pointwise mutual information together with instance weighting. We demonstrate that the proposed objective is able to attenuate the effect of false positive pairs while efficiently exploiting the structure in the pretrained feature space. As a result, we improve the clustering accuracy over $k$-means on $17$ different pretrained models by $6.1$\% and $12.2$\% on ImageNet and CIFAR100, respectively. Finally, using self-supervised vision transformers, we achieve a clustering accuracy of $61.6$\% on ImageNet. The code is available at https://github.com/HHU-MMBS/TEMI-official-BMVC2023.

• The paper introduces a self-distillation clustering framework that leverages pretrained feature spaces to boost performance over conventional methods.
• It employs a novel pointwise mutual information objective with instance weighting to refine cluster assignments amid noisy pairings.
• Empirical evaluations on ImageNet and CIFAR100 show up to 12.2% accuracy improvement over k-means, demonstrating the method’s effectiveness.

Exploring the Limits of Deep Image Clustering using Pretrained Models

The paper "Exploring the Limits of Deep Image Clustering using Pretrained Models" by Adaloglou et al. explores the challenges and methodologies associated with unsupervised image clustering by leveraging the power of pretrained vision models. The investigation emphasizes the pivotal role played by representation learning, particularly when these representations are derived from pretrained architectures. The paper presents a novel self-distillation methodology designed to improve the precision of clustering labels in the absence of explicit data annotations.

Methodology and Contributions

Adaloglou et al. introduce a self-distillation training algorithm that adapts to the feature space constructed by pretrained models. Given the significant alignment between nearest neighbors in this feature space with similar labels, they propose leveraging these relationships. This paper's central innovation lies in the introduction of an objective function based on pointwise mutual information, augmented by instance weighting to handle noisy pairings of features.

Key contributions of the paper include:

1. Pointwise Mutual Information (PMI) Objective: The authors propose an objective that emphasizes learning associations between image features through a variant of PMI. This approach adeptly capitalizes on the structure inherent in the pretrained feature space while mitigating the effects of false positive feature pairings.
2. Self-Distillation Clustering Framework: Introducing a teacher-student paradigm within the clustering framework allows the model to self-refine its cluster assignments without supervision. This methodology is grounded in a careful balance of predicted class distributions, achieved via a temperature-scaled softmax function.
3. Comprehensive Evaluation & Statistical Gains: The robustness of the proposed system is demonstrated through empirical evaluations on ImageNet and CIFAR100, where it surpasses $k$-means accuracy by 6.1% and 12.2%, respectively. These statistical results underline the efficacy of the proposed framework over existing clustering methodologies.
4. Performance with Self-Supervised Learning: Utilizing self-supervised vision transformers, the paper achieves a clustering accuracy of 61.6% on ImageNet, marking a significant improvement over conventional methods.

Implications and Future Directions

The implications of this research extend to the broad field of unsupervised machine learning applications, especially in scenarios where labeled data is scarce or expensive to obtain. The potential of leveraging pretrained architectures for tasks beyond their routine applications could lead to more adaptable AI systems capable of nuanced image understanding and categorization.

Theoretically, this research paves the way for further exploration into unsupervised learning objectives that exploit mutual information from pretrained features. This direction could uncover more efficient and scalable solutions for clustering tasks across various domains.

Future research avenues might include enhancing the scalability of the proposed methods to accommodate even larger datasets, integrating these clustering systems into broader automated pipelines, and exploring the balance of unsupervised learning objectives with minimal supervisory cues.

Overall, this paper provides a substantial contribution to the field of unsupervised image clustering, drawing on the potent capabilities of pretrained models. The authors' methodological innovations offer a nuanced and practical approach to understanding and decomposing complex visual datasets, setting a benchmark for future explorations in this domain.