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Found 4 papers, 2 papers with code
Generalized Knowledge Distillation via Relationship Matching
Instead of enforcing the teacher to work on the same task as the student, we borrow the knowledge from a teacher trained from a general label space -- in this "Generalized Knowledge Distillation (GKD)", the classes of the teacher and the student may be the same, completely different, or partially overlapped.
Selective Cross-Task Distillation
Our method reuses cross-task knowledge from a distinct label space and efficiently assesses teachers without enumerating the model repository.
Few-Shot Action Recognition with Compromised Metric via Optimal Transport
In detail, given two videos, we sample segments from them and cast the calculation of their distance as an optimal transport problem between two segment sequences.
Towards Enabling Meta-Learning from Target Models
Different from $\mathcal{S}$/$\mathcal{Q}$ protocol, we can also evaluate a task-specific solver by comparing it to a target model $\mathcal{T}$, which is the optimal model for this task or a model that behaves well enough on this task ($\mathcal{S}$/$\mathcal{T}$ protocol).
