Learning Representation for Clustering via Prototype Scattering and Positive Sampling (2111.11821v2)

Abstract: Existing deep clustering methods rely on either contrastive or non-contrastive representation learning for downstream clustering task. Contrastive-based methods thanks to negative pairs learn uniform representations for clustering, in which negative pairs, however, may inevitably lead to the class collision issue and consequently compromise the clustering performance. Non-contrastive-based methods, on the other hand, avoid class collision issue, but the resulting non-uniform representations may cause the collapse of clustering. To enjoy the strengths of both worlds, this paper presents a novel end-to-end deep clustering method with prototype scattering and positive sampling, termed ProPos. Specifically, we first maximize the distance between prototypical representations, named prototype scattering loss, which improves the uniformity of representations. Second, we align one augmented view of instance with the sampled neighbors of another view -- assumed to be truly positive pair in the embedding space -- to improve the within-cluster compactness, termed positive sampling alignment. The strengths of ProPos are avoidable class collision issue, uniform representations, well-separated clusters, and within-cluster compactness. By optimizing ProPos in an end-to-end expectation-maximization framework, extensive experimental results demonstrate that ProPos achieves competing performance on moderate-scale clustering benchmark datasets and establishes new state-of-the-art performance on large-scale datasets. Source code is available at \url{https://github.com/Hzzone/ProPos}.

An Essay on "Learning Representation for Clustering via Prototype Scattering and Positive Sampling"

The paper "Learning Representation for Clustering via Prototype Scattering and Positive Sampling" introduces an innovative deep clustering methodology designed to bridge the gaps inherent in existing contrastive and non-contrastive learning frameworks. The key proposition, named ProPos, leverages prototype scattering and positive sampling to produce uniform representations while avoiding class collision issues typically associated with contrastive approaches.

Contrastive and Non-Contrastive Learning Issues

Contrastive learning methods, though highly effective for representation learning, are often plagued by the class collision problem — where semantically similar samples are incorrectly repelled as negative pairs. This can significantly hinder the clustering performance by disrupting the semantic consistencies in the data representation. Non-contrastive methods, while sidestepping class collision by avoiding negative examples, tend to suffer from representation collapse due to the absence of a mechanism to enforce uniformity across the learned features.

The Proposed ProPos Framework

ProPos addresses these limitations using two main techniques: Prototype Scattering Loss (PSL) and Positive Sampling Alignment (PSA).

1. Prototype Scattering Loss (PSL): This loss function aims to improve representation uniformity by increasing the inter-cluster distance. Prototypes derived from distinct clusters are treated as negative pairs, mimicking a prototypical contrastive loss at the cluster level without generating class collision. By maximizing this separation among cluster prototypes, PSL helps maintain uniformity in the feature representations, which is essential for clustering stability.
2. Positive Sampling Alignment (PSA): Moving away from negative sampling, the PSA technique samples neighboring points in the embedding space around the anchor point, complying with a Gaussian distribution. This process assumes these sampled neighbors are positively related to the original point and thus, belongs to the same cluster, reinforcing cluster compactness.

Empirical Justification and Results

Empirical validations highlighted in the paper show ProPos's consistently superior performance on multiple benchmark datasets such as CIFAR-10, ImageNet-10, and Tiny-ImageNet. The evaluation metrics, including Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI), reveal that ProPos not only achieves but in many cases surpasses the previous state-of-the-art results in clustering accuracy.

The paper also delineates the avoidance of the feature collapse issue, which is endemic to non-contrastive learning methods, showcasing the stability of ProPos over extensive training epochs. This stability is reflected in its capacity to generate well-balanced clustering outcomes even on challenging datasets with fine-grained classes, such as ImageNet-Dogs.

Broader Implications and Future Directions

The implementation of ProPos underscores significant implications for both theoretical advancement and practical deployment of unsupervised learning systems. By combining the strengths of both contrastive and non-contrastive methodologies, it offers a compelling framework for applications requiring robust feature learning without labeled data, such as anomaly detection and unsupervised domain adaptation.

Looking forward, ProPos sets a promising direction towards improving clustering algorithms by further innovating in the domains of prototype-based learning. Future research could explore adapting ProPos to dynamic environments where the number of clusters is not predetermined or integrating it with semi-supervised frameworks to harness partial label information for even more precise clustering performance.

In conclusion, "Learning Representation for Clustering via Prototype Scattering and Positive Sampling" presents a significant step towards marrying the strengths of existing self-supervised learning paradigms while effectively tackling their inherent weaknesses, making it a noteworthy contribution to the field of machine learning.