SeBA is a joint-embedding framework that separates tabular data into two complementary views and aligns one view's representations to the nearest-neighbor structure of the other, improving feature-label relationships and achieving SOTA results in most benchmarks without relying on augmentations.
Parkhi and Andrea Vedaldi and Andrew Zisserman and C
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A novel loss function enables effective segmentation training from summary statistics combined with minimal weak pixel supervision, outperforming statistics alone on medical imaging tasks.
citing papers explorer
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SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data
SeBA is a joint-embedding framework that separates tabular data into two complementary views and aligns one view's representations to the nearest-neighbor structure of the other, improving feature-label relationships and achieving SOTA results in most benchmarks without relying on augmentations.
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Learning to Segment using Summary Statistics and Weak Supervision
A novel loss function enables effective segmentation training from summary statistics combined with minimal weak pixel supervision, outperforming statistics alone on medical imaging tasks.