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Found 2 papers, 1 papers with code
Approximate Bijective Correspondence for isolating factors of variation
We propose a novel algorithm that relies on a weak form of supervision where the data is partitioned into sets according to certain \textit{inactive} factors of variation.
Leveraging affinity cycle consistency to isolate factors of variation in learned representations
In this work, we operate in the setting where limited information is known about the data in the form of groupings, or set membership, and the task is to learn representations which isolate the factors of variation that are common across the groupings.
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