  Generalization in audio deepfake detection presents a significant challenge,
with models trained on specific datasets often struggling to detect deepfakes
generated under varying conditions and unknown algorithms. While collectively
training a model using diverse datasets can enhance its generalization ability,
it comes with high computational costs. To address this, we propose a neural
collapse-based sampling approach applied to pre-trained models trained on
distinct datasets to create a new training database. Using ASVspoof 2019
dataset as a proof-of-concept, we implement pre-trained models with Resnet and
ConvNext architectures. Our approach demonstrates comparable generalization on
unseen data while being computationally efficient, requiring less training
data. Evaluation is conducted using the In-the-wild dataset.
