What is recorded here?
Learned visual features by predicting cluster assignments across augmented views and introduced mixed-resolution multi-crop training to improve representation learning without exhaustive pairwise feature comparisons.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Clustering, balanced assignments and augmentation are inherited foundations. Not a new supervised label source or a guarantee of collapse-free optimization for all configurations.
Read the original work
- Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
- Caron et al.: SwAV swapped cluster prediction and multi-crop training — author/institution implementation
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People and systems
Mathilde Caron; Ishan Misra; Julien Mairal; Priya Goyal; Piotr Bojanowski; Armand Joulin
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English catalogue narrative
Caron et al.: SwAV swapped cluster prediction and multi-crop training
Learned visual features by predicting cluster assignments across augmented views and introduced mixed-resolution multi-crop training to improve representation learning without exhaustive pairwise feature comparisons.
Scientific scope
Clustering, balanced assignments and augmentation are inherited foundations. Not a new supervised label source or a guarantee of collapse-free optimization for all configurations.
People and systems
Mathilde Caron; Ishan Misra; Julien Mairal; Priya Goyal; Piotr Bojanowski; Armand Joulin