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Found 4 papers, 1 papers with code
Bayesian Evidential Learning for Few-Shot Classification
Few-Shot Classification(FSC) aims to generalize from base classes to novel classes given very limited labeled samples, which is an important step on the path toward human-like machine learning.
Switchable Representation Learning Framework with Self-compatibility
Existing research mainly focuses on the one-to-one compatible paradigm, which is limited in learning compatibility among multiple models.
Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation
Therefore, backward-compatible representation is proposed to enable "new" features to be compared with "old" features directly, which means that the database is active when there are both "new" and "old" features in it.
Dual-Tuning: Joint Prototype Transfer and Structure Regularization for Compatible Feature Learning
It is a heavy workload to re-extract features of the whole database every time. Feature compatibility enables the learned new visual features to be directly compared with the old features stored in the database.
