Decompose-and-Compose: Addressing Spurious Correlation in Image Classification

Core Concepts

The author proposes the Decompose-and-Compose (DaC) method to address spurious correlations in image classification by decomposing images into causal and non-causal parts and combining them to mitigate correlation shift.

Abstract

The content discusses the challenges of spurious correlations in image classification and introduces the DaC method to improve robustness by decomposing images into causal and non-causal components, intervening on images, and creating new data points for group balancing. The approach is shown to outperform other methods on various benchmarks.
The standard Empirical Risk Minimization (ERM) training is effective for image classification but fails on out-of-distribution samples due to spurious correlations. The proposed DaC method aims to improve robustness by addressing correlation shifts through a compositional approach based on combining elements of images.
By identifying causal components using class activation maps, intervening on images, and retraining models with augmented data, DaC provides better worst group accuracy compared to previous methods without requiring group labels during training.
Through experiments on different datasets with distribution shifts, the DaC method demonstrates superior performance in mitigating spurious correlations and achieving robustness in image classification tasks.

Stats

According to the study, DFR retrains the last layer of a model previously trained by ERM with group-balanced data for robustness against spurious correlation.
MaskTune fine-tunes models using masked versions of training data to focus on core features instead of spurious correlations.
CNC aims at aligning representations within the same class that have different spurious attributes while distinguishing between similar attributes across dissimilar classes.
Group DRO minimizes worst-case loss across groups with strong regularization but requires expensive group annotations for each training point.
JTT detects misclassified samples using ERM and upweights them for retraining without needing group labels during training.

Quotes

"Models trained with ERM usually highly attend to either the causal components or those having high spurious correlation with the label."
"Our proposed method performs better than previous baselines on well-known benchmarks in literature."
"DaC combines different images and uses them for model distillation at the representation level."
"The proposed method does not require group labels during training yet outperforms Group DRO and LISA on various datasets."

Key Insights Distilled From

Decompose-and-Compose

by Fahimeh Hoss... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18919.pdf

Deeper Inquiries

How can the concept of decomposing images into causal and non-causal parts be extended beyond image classification tasks

The concept of decomposing images into causal and non-causal parts can be extended beyond image classification tasks to various other domains where understanding the underlying factors influencing a decision is crucial. For example:

Medical Imaging: In medical imaging, identifying causal components in an image could help in diagnosing diseases more accurately by focusing on relevant features while ignoring artifacts or irrelevant details.
Autonomous Vehicles: Decomposing images into causal and non-causal parts can aid in better object detection and recognition for autonomous vehicles, ensuring that decisions are based on essential visual cues rather than distractions.
Natural Language Processing: Extending this concept to text data could involve separating out informative content from noise or bias, improving the performance of NLP models.
By applying this decomposition approach across different domains, it becomes possible to enhance model interpretability, reduce reliance on spurious correlations, and improve generalization capabilities beyond traditional image classification tasks.

What are potential limitations or drawbacks of relying solely on attribution maps for decomposing images

While attribution maps provide valuable insights into which parts of an image contribute most to a model's prediction, there are potential limitations when relying solely on them for decomposing images:

Interpretability Challenges: Attribution maps may not always capture complex interactions between different components of an image accurately. They might oversimplify the contribution of each part without considering holistic context.
Robustness Issues: Attribution maps are sensitive to changes in input data distribution or perturbations. This sensitivity can lead to instability in attributions and potentially misguide interventions based on these maps.
Limited Contextual Understanding: Attribution maps focus only on local pixel-level importance without considering broader contextual information within an image. This limitation may result in missing out on higher-level semantic relationships.
To address these drawbacks, it is essential to complement attribution map analysis with other techniques like feature visualization methods or causal reasoning approaches for a more comprehensive understanding of how models make predictions based on different components within an image.

How might interventions based on combining image components impact interpretability or generalization capabilities of models

Interventions based on combining image components can have both positive and negative impacts on model interpretability and generalization capabilities:
Positive Impacts

Improved Robustness: By creating new datapoints through combinations, models become less reliant on spurious correlations present in individual samples.
Enhanced Generalization: Introducing novel compositions during training helps models learn diverse patterns present across different groups or classes.
Group Balancing: The intervention method provides a way to balance representation from minority groups without requiring explicit group annotations.

Negative Impacts

Loss of Interpretability: Combining multiple images may make it challenging to interpret why certain decisions are made by the model since the relationship between original inputs is altered.
Overfitting Risk: If not carefully controlled, interventions through combination could lead to overfitting if synthetic data dominate training examples excessively.
Complexity Increase: The process of combining images adds complexity to the training pipeline and may require additional computational resources for processing large datasets efficiently.

Balancing these trade-offs effectively is crucial when implementing interventions based on combined image components to ensure optimal model performance while maintaining interpretability standards.