Communication Dans Un Congrès Année : 2024

On Transfer in Classification: How Well do Subsets of Classes Generalize?

Résumé

In classification settings, models trained on certain classes may apply to unseen ones, indicating learning beyond the original task. This concept is frequently utilized in transfer learning, where pretrained models can handle new classes. However, the theoretical underpinnings of this phenomenon remain largely unexplored. This study aims to establish a theoretical framework for class transferability. We introduce a partial order relation for class sets, enabling the characterization of class sets that can generalize to others. In practical terms, we investigate our framework's capacity to predict which class sets yield optimal performance when tested on new classes. Additionally, we delve into fewshot learning, where transfer is essential. Our work contributes to better understanding of transfer mechanics and model generalization.

Dates et versions

hal-05002785 , version 1 (24-03-2025)

Identifiants

Citer

Raphael Baena, Lucas Drumetz, Vincent Gripon. On Transfer in Classification: How Well do Subsets of Classes Generalize?. EUSIPCO 2024: 32nd European Signal Processing Conference, Aug 2024, Lyon, France. pp.1836-1840, ⟨10.23919/EUSIPCO63174.2024.10715243⟩. ⟨hal-05002785⟩
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