Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations
This addresses the challenge of deploying models in new environments for applications like wildlife monitoring and medical imaging, offering an incremental improvement over existing methods.
The paper tackles the problem of poor out-of-distribution generalization in domain adaptation by showing that standard fine-tuning after self-supervised pretraining does not consistently improve performance, and proposes Connect Later, which uses targeted augmentations during fine-tuning to better connect domains, achieving state-of-the-art results on real-world datasets with improvements such as 2.5% on astronomical classification and 1.1% on tumor identification.
Models trained on a labeled source domain (e.g., labeled images from wildlife camera traps) often generalize poorly when deployed on an out-of-distribution (OOD) target domain (e.g., images from new camera trap locations). In the domain adaptation setting where unlabeled target data is available, self-supervised pretraining (e.g., masked autoencoding or contrastive learning) is a promising method to mitigate this performance drop. Pretraining improves OOD error when the generic data augmentations used (e.g., masking or cropping) connect the source and target domains, which may be far apart in the input space. In this paper, we show on real-world tasks that standard fine-tuning after pretraining does not consistently improve OOD error over simply training from scratch on labeled source data. To better leverage pretraining for distribution shifts, we propose Connect Later: after pretraining with generic augmentations, fine-tune with targeted augmentations designed with knowledge of the distribution shift. Pretraining learns good representations within the source and target domains, while targeted augmentations connect the domains better during fine-tuning. Connect Later improves average OOD error over standard fine-tuning and supervised learning with targeted augmentations on 4 real-world datasets: Connect Later achieves the state-of-the-art on astronomical time-series classification (AstroClassification) by 2.5%, wildlife species identification (iWildCam-WILDS) with ResNet-50 by 0.9%, and tumor identification (Camelyon17-WILDS) with DenseNet121 by 1.1%; as well as best performance on a new dataset for astronomical time-series redshift prediction (Redshifts) by 0.03 RMSE (11% relative). Code and datasets are available at https://github.com/helenqu/connect-later.