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Found 4 papers, 2 papers with code
On Transfer in Classification: How Well do Subsets of Classes Generalize?
In classification, it is usual to observe that models trained on a given set of classes can generalize to previously unseen ones, suggesting the ability to learn beyond the initial task.
Preserving Fine-Grain Feature Information in Classification via Entropic Regularization
In this paper we are interested in the problem of solving fine-grain classification or regression, using a model trained on coarse-grain labels only.
Preventing Manifold Intrusion with Locality: Local Mixup
Mixup is a data-dependent regularization technique that consists in linearly interpolating input samples and associated outputs.
Ranked #9 on
Image Classification
on Fashion-MNIST
Inferring Graph Signal Translations as Invariant Transformations for Classification Tasks
The field of Graph Signal Processing (GSP) has proposed tools to generalize harmonic analysis to complex domains represented through graphs.
