Decoupling Style and Spurious Features to Achieve Invariant Representation for Out-of-Distribution Generalization

Основні поняття

The core message of this paper is to propose a new framework called IRSS that can gradually separate style distribution and spurious features from images by introducing adversarial neural networks and multi-environment optimization, thus achieving out-of-distribution generalization without requiring additional supervision such as domain labels.

Анотація

This paper addresses the out-of-distribution (OOD) generalization problem under the setting that both style distribution shift and spurious features exist, and domain labels are missing.
The key insights are:

The authors analyze public OOD benchmark datasets and identify two essential factors that lead to distribution shift: diverse styles and spurious features (i.e., objects outside the target class of interest).
The authors propose a new Structural Causal Model (SCM) for image generation that explicitly captures style and spurious features, with no direct causal relationship between the image and the label, nor between the spurious feature and the style.
Based on the proposed SCM, the authors develop a new framework called IRSS (Invariant Representation Learning via Decoupling Style and Spurious Features). IRSS aligns the style distribution and eliminates the influence of spurious features using two independent components, without requiring domain labels.
IRSS outperforms traditional OOD methods and solves the problem of Invariant Risk Minimization (IRM) degradation, enabling the extraction of invariant features under distribution shift.

The authors conduct experiments on benchmark datasets PACS, OfficeHome, and NICO, demonstrating the effectiveness of IRSS in achieving out-of-distribution generalization.

Статистика

The PACS dataset consists of four domains (Art Painting, Cartoon, Photo, and Sketch) with seven common categories.
The OfficeHome dataset consists of four domains (Art, Clipart, Product, and RealWorld) with 65 different categories.
The NICO dataset contains 19 classes with 9 or 10 different contexts (object poses, positions, backgrounds, movement patterns, etc.).

Цитати

"This form of distribution discrepancy in OOD is called style distribution shift."
"This form of distribution discrepancy in OOD is called spurious feature shift."

Ключові висновки, отримані з

Invariant Representation via Decoupling Style and Spurious Features from Images

by Ruimeng Li,Y... о arxiv.org 04-02-2024

https://arxiv.org/pdf/2312.06226.pdf

Глибші Запити

How can the proposed IRSS framework be extended to handle more complex distribution shifts, such as those involving multiple types of spurious features or more diverse styles

The IRSS framework can be extended to handle more complex distribution shifts by incorporating additional components to address multiple types of spurious features or diverse styles. One approach could involve enhancing the feature extraction process to identify and separate different types of spurious features present in the data. This could involve developing specialized modules within the framework to detect and mitigate the influence of various spurious features. Additionally, the style distribution alignment component could be expanded to accommodate a wider range of diverse styles by incorporating more sophisticated clustering algorithms or feature mapping techniques. By enhancing the framework's ability to handle multiple types of spurious features and diverse styles, IRSS can better adapt to complex distribution shifts in real-world datasets.

What are the potential limitations of the Structural Causal Model used in IRSS, and how could it be further refined to better capture the underlying data generation process

The potential limitations of the Structural Causal Model (SCM) used in IRSS lie in its assumptions about the underlying data generation process. One limitation is the simplifying assumption of independence between causal features, style features, and spurious features. To address this, the SCM could be refined by incorporating more complex relationships and interactions between these features. For example, introducing non-linear relationships or feedback loops in the model could better capture the intricate dependencies in the data generation process. Additionally, the SCM could benefit from incorporating latent variables to account for unobserved factors that may influence the image generation process. By refining the SCM to better reflect the complexities of real-world data generation, IRSS can improve its ability to disentangle style and spurious features effectively.

Given the importance of eliminating the influence of spurious features, how could the IRSS framework be adapted to work with other types of data beyond images, where the notion of spurious features may be less clear-cut

Adapting the IRSS framework to work with other types of data beyond images involves redefining the concept of spurious features in a more general context. In non-image data, spurious features may manifest as irrelevant variables, confounding factors, or noise that can impact the model's generalization performance. To address this, the framework can be modified to incorporate domain-specific knowledge about the data and identify the sources of spurious features unique to that domain. This adaptation may involve developing domain-specific preprocessing steps to identify and remove spurious features, as well as integrating domain knowledge into the feature extraction and alignment processes. By customizing the framework to handle different types of data and their associated spurious features, IRSS can be applied to a broader range of domains and datasets effectively.