  Intelligent systems possess a crucial characteristic of breaking complicated
problems into smaller reusable components or parts and adjusting to new tasks
using these part representations. However, current part-learners encounter
difficulties in dealing with incidental correlations resulting from the limited
observations of objects that may appear only in specific arrangements or with
specific backgrounds. These incidental correlations may have a detrimental
impact on the generalization and interpretability of learned part
representations. This study asserts that part-based representations could be
more interpretable and generalize better with limited data, employing two
innovative regularization methods. The first regularization separates
foreground and background information's generative process via a unique
mixture-of-parts formulation. Structural constraints are imposed on the parts
using a weakly-supervised loss, guaranteeing that the mixture-of-parts for
foreground and background entails soft, object-agnostic masks. The second
regularization assumes the form of a distillation loss, ensuring the invariance
of the learned parts to the incidental background correlations. Furthermore, we
incorporate sparse and orthogonal constraints to facilitate learning
high-quality part representations. By reducing the impact of incidental
background correlations on the learned parts, we exhibit state-of-the-art
(SoTA) performance on few-shot learning tasks on benchmark datasets, including
MiniImagenet, TieredImageNet, and FC100. We also demonstrate that the
part-based representations acquired through our approach generalize better than
existing techniques, even under domain shifts of the background and common data
corruption on the ImageNet-9 dataset. The implementation is available on
GitHub: https://github.com/GauravBh1010tt/DPViT.git

Machine Learning