Deep Clustering for Unsupervised Learning of Visual Features (1807.05520v2)

Abstract: Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end training of visual features on large scale datasets. In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features. DeepCluster iteratively groups the features with a standard clustering algorithm, k-means, and uses the subsequent assignments as supervision to update the weights of the network. We apply DeepCluster to the unsupervised training of convolutional neural networks on large datasets like ImageNet and YFCC100M. The resulting model outperforms the current state of the art by a significant margin on all the standard benchmarks.

• The paper demonstrates that integrating k-means clustering with CNN training effectively learns robust visual features from unlabeled images.
• Experimental results show DeepCluster outperforms existing methods, boosting ImageNet linear classification accuracy by approximately 5-10%.
• The approach is robust across various CNN architectures and datasets, making it adaptable for domains with limited annotated data.

Deep Clustering for Unsupervised Learning of Visual Features

This paper, authored by Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze from Facebook AI Research, makes significant contributions to the field of unsupervised learning in computer vision. The core objective is to develop a method for extracting useful visual features from images without relying on labeled data, a task that typically necessitates large-scale manual annotation.

Overview of DeepCluster Method

The researchers introduce DeepCluster, an iterative clustering technique that leverages the synergy between feature learning and clustering. The approach integrates $k$-means clustering and neural network training in an end-to-end unsupervised learning pipeline. Specifically, it alternates between:

1. Clustering image features produced by a convolutional neural network (CNN) using $k$-means.
2. Using the cluster assignments as pseudo-labels to update the CNN weights through standard backpropagation.

Performance and Comparisons

Experimental Validation: The proposed method achieves state-of-the-art performance on unsupervised learning benchmarks, significantly outperforming prior techniques on tasks like ImageNet classification and transfer learning to other datasets. For instance, DeepCluster demonstrates marked improvements over existing methods by around 5-10% in linear classification tasks on ImageNet and Places datasets using frozen convolutional features.

Robustness Analysis: The robustness of the method was validated through experiments involving variations in the CNN architecture (e.g., AlexNet to VGG) and different training sets (ImageNet versus YFCC100M). Remarkably, DeepCluster maintains competitive performance even when trained on the less curated and more diverse YFCC100M dataset, thereby underscoring its adaptability to varying data distributions.

Technical Insights

Avoidance of Trivial Solutions:

1. Empty Clusters: The authors mitigate empty cluster issues during $k$-means by reassigning empty clusters with slightly perturbed centroids from non-empty clusters.
2. Trivial Parametrization: To prevent the model from collapsing to trivial solutions (e.g., assigning all data points to a single cluster), they balance the contribution of each cluster during training by sampling based on a uniform distribution over the clusters.

Comparative Analysis:

1. Alternative Clustering Algorithms: The efficacy of DeepCluster was also compared against Power Iteration Clustering (PIC), a graph-based clustering method. Despite the intrinsic differences between these algorithms, the results suggested comparable performance, with $k$-means showing slightly better results in typical setups.
2. Diverse Datasets and Architectures: The method's efficacy was not significantly hampered by variations, indicating its robustness across different domains and network architectures.

Theoretical and Practical Implications

The proposed approach broadens the scope of unsupervised learning by demonstrating that effective feature extraction does not necessarily depend on the complexity of the clustering algorithm or the dataset's nature. The minimal assumptions about input data required by DeepCluster position it as a flexible tool for domains where annotated data is sparse or expensive to obtain.

Implications of Findings:

• Theoretical: The findings challenge the dominant narrative that supervised pre-training is a prerequisite for performant feature extraction in deep learning. This contributes to the ongoing discourse about the potential of unsupervised learning paradigms to rival supervised methods.
• Practical: Practitioners can leverage DeepCluster to build models for new domains without extensive manual annotation efforts, democratizing the application of deep learning in niche fields such as medical imaging and satellite imagery.

Future Developments

Potential Extensions:

• Scaling to Larger Architectures and Datasets: Given the success with AlexNet and VGG, extending the method to newer architectures like ResNet or EfficientNet on even larger datasets could provide further insights.
• Combining Multiple Clues: Future work could explore the integration of self-supervised learning tasks alongside clustering, enhancing the richness of the learned features.

Evaluation on Broader Benchmarks:

• Expanding the evaluation to include more instance-level recognition tasks, such as image retrieval, could provide comprehensive assessments of the method's ability to capture fine-grained details in visual data.

In summary, this paper significantly advances unsupervised learning by introducing a scalable and robust method for visual feature extraction, with demonstrated performance improvements over existing approaches and wide-ranging implications for both theoretical research and practical applications.