Classification fields are infinite recursive hierarchical cluster structures generated by a local refinement rule, and a ReLU network predictor learned from finite prefixes can approximate the generator and extend it to deeper levels with exponential convergence in the completed cell metric.
Deep clustering for unsupervised learning of visual features
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Spatial Prediction pretext task learns spatial structure in self-supervised learning by regressing relative position and scale between image views, yielding more structured representations and better generalization.
citing papers explorer
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Classification Fields: Arbitrarily Fine Recursive Hierarchical Clustering From Few Examples
Classification fields are infinite recursive hierarchical cluster structures generated by a local refinement rule, and a ReLU network predictor learned from finite prefixes can approximate the generator and extend it to deeper levels with exponential convergence in the completed cell metric.
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Learning to Perceive "Where": Spatial Pretext Tasks for Robust Self-Supervised Learning
Spatial Prediction pretext task learns spatial structure in self-supervised learning by regressing relative position and scale between image views, yielding more structured representations and better generalization.