Learning to Discover Novel Visual Categories via Deep Transfer Clustering (1908.09884v1)

Abstract: We consider the problem of discovering novel object categories in an image collection. While these images are unlabelled, we also assume prior knowledge of related but different image classes. We use such prior knowledge to reduce the ambiguity of clustering, and improve the quality of the newly discovered classes. Our contributions are twofold. The first contribution is to extend Deep Embedded Clustering to a transfer learning setting; we also improve the algorithm by introducing a representation bottleneck, temporal ensembling, and consistency. The second contribution is a method to estimate the number of classes in the unlabelled data. This also transfers knowledge from the known classes, using them as probes to diagnose different choices for the number of classes in the unlabelled subset. We thoroughly evaluate our method, substantially outperforming state-of-the-art techniques in a large number of benchmarks, including ImageNet, OmniGlot, CIFAR-100, CIFAR-10, and SVHN.

• The paper introduces an enhanced DEC method that leverages transfer learning to effectively discover novel visual categories in unlabeled data.
• It modifies the original DEC by adding a tight bottleneck layer, temporal ensembling, and consistency constraints while estimating unknown class counts via a semi-supervised approach.
• Experimental evaluations on benchmarks such as ImageNet, CIFAR-10/100, SVHN, and OmniGlot demonstrate significantly improved clustering accuracy and normalized mutual information.

An Exploration into Deep Transfer Clustering for Novel Visual Category Discovery

The research paper entitled "Learning to Discover Novel Visual Categories via Deep Transfer Clustering" by Kai Han, Andrea Vedaldi, and Andrew Zisserman contributes to the field of unsupervised learning through the introduction of a novel methodology for discovering categories in unlabelled datasets by leveraging prior knowledge from labelled datasets. The authors present a comprehensive paper on extending the Deep Embedded Clustering (DEC) algorithm into a transfer learning context, proposing notable improvements and modifications that have demonstrated significant outperformance over existing state-of-the-art techniques on various benchmarks.

Methodology and Contributions

The paper delineates two major contributions: an extension of DEC to a transfer setting with various algorithmic enhancements, and a robust mechanism for estimating the number of novel categories in unlabelled datasets.

1. Modified Deep Embedded Clustering: The authors revisit the classical DEC to introduce a tight bottleneck layer, temporal ensembling, and consistency constraints, optimizing it for discovering novel categories. The revised method not only clusters the data but simultaneously learns discriminative data representations through the network, which is pre-trained on a related labelled dataset. The modifications enable the mechanism to leverage that initial representation while adapting it to novel categories in the absence of explicit supervision.
2. Estimating Novel Categories: Addressing a critical aspect of cluster analysis, the paper introduces a method that estimates the number of classes in unlabelled data by deploying a semi-supervised k-means approach using probe sets drawn from known classes. This innovative use of labelled data to optimize estimates enhances both the flexibility and reliability of the clustering process.

Experimental Evaluation

The extensive experimentation conducted with public benchmarks, including ImageNet, CIFAR-100, CIFAR-10, SVHN, and OmniGlot, underscores the efficacy of the proposed methodology. The experimental results reveal that the introduced methods consistently outperform traditional and contemporary learning-based clustering methods under both known and unknown category conditions. Notably, the use of temporal ensembling and consistency modeling yielded superior clustering accuracy (ACC) and normalized mutual information (NMI) scores compared to the baseline DEC.

Implications and Future Directions

The practical implications of this research are substantial, especially in domains requiring automated structuring of complex visual data without comprehensive labelling, such as market research or wildlife monitoring. From a theoretical standpoint, the paper enriches the dialogue on the role of transferable representations in unsupervised learning. The methodologies presented open several pathways for future research. Potential areas of further exploration include extending the framework to integrate self-supervised learning features, incorporating additional domain adaptation mechanisms, or exploring its applicability to non-visual data contexts.

In conclusion, the authors showcase not only a robust model but also present a methodology with wide-reaching implications in machine learning. Their integration of transfer learning principles into clustering represents a strategic stride forward, demonstrating that leveraging pre-existing knowledge can enhance novel discovery tasks in machine learning effectively.