Work

TEP-ones: A simple yet effective approach for transferability estimation of pruned backbones

Year: 2025

Type: article

Abstract: In deep learning, the conventional transfer learning paradigm involves fine-tuning a model pre-trained on a complex source task to adapt it to a simpler target task, capitalizing on abundant training data. Concurrently, the paradigm of neural network pruning has emerged as a powerful strategy for enhancing model efficiency, reducing complexity, and optimizing resource utilization. This paper focuses on pruned model transferability estimation for resource-constraint scenarios, where the goal is to rank the performance of pruned pre-trained models on a downstream task without fine-tuning. To this end, from a formal analysis of the intra-class mutual information between samples belonging to the same target class, we observe that, as pruning increases, a sweet phase naturally rises, where the model benefits from better features at the encoder’s output. From this, we derive a Transferability Estimation for Pruned Backbones (TEP-ones) that eases the choice of which pruned model (without the need to train the classifier) is the best candidate for transfer learning.

Source: Neurocomputing

DOI: 10.1016/j.neucom.2025.132209