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: this https URL

通过创新的正则化方法，本研究指出基于部件的表征可以通过两种方法变得更易解释且泛化能力更强，其中第一种方法通过唯一的部分混合形式将前景和背景信息的生成过程分开，通过弱监督损失对部分施加结构约束，保证前景和背景的部分混合具有软性、与对象无关的掩码。第二个正则化方法则采用蒸馏损失的形式，确保学到的部分对于背景的偶发相关性具有不变性。通过减少偶发背景相关性对学习到的部分的影响，我们在 MiniImagenet、TieriImageNet 和 FC100 等基准数据集上展示出了最先进的少样本学习任务性能，并且还证明了通过我们的方法获得的基于部件的表征在 ImageNet-9 数据集上即使在背景领域转移和常见数据损坏下也能更好地泛化。

缓解零碎相关性对基于部分的学习的影响

Mitigating the Effect of Incidental Correlations on Part-based Learning

Part-based representations have been shown to be very useful for image
classification. Learning part-based models is often viewed as a two-stage
problem. First, a collection of informative parts is discovered, using
heuristics that promote part distinctiveness and diversity, and then
classifiers are trained on the vector of part responses. In this paper we unify
the two stages and learn the image classifiers and a set of shared parts
jointly. We generate an initial pool of parts by randomly sampling part
candidates and selecting a good subset using L1/L2 regularization. All steps
are driven "directly" by the same objective namely the classification loss on a
training set. This lets us do away with engineered heuristics. We also
introduce the notion of "negative parts", intended as parts that are negatively
correlated with one or more classes. Negative parts are complementary to the
parts discovered by other methods, which look only for positive correlations.

本论文介绍了一种可学习的基于特征组的图像分类方法，通过联合学习图像分类器和一组共享的部分来实现，避免了使用引擎启发式算法的过程，并引入了负部分的概念来补足其他方法只寻找正相关性的缺陷。