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SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments (Mathilde Caron)

Machine Learning Street Talk (MLST)

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Clustering Complexities in Image Recognition

This chapter examines the intricacies of clustering techniques in image recognition, emphasizing how human perception influences clustering outcomes. It contrasts curated datasets like ImageNet with uncurated data and investigates the implications of cluster counts on performance in unsupervised learning. The discussion highlights the importance of data transformation and empirical testing in determining effective clustering strategies across various domains, including natural language processing.

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