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Mitigating Reliance on Spurious Correlations in Deep Neural Classifiers without Annotation

Core Concepts
A self-guided framework that automatically detects and mitigates a classifier's reliance on spurious correlations in the data without requiring any prior annotations.
The paper proposes a novel self-guided framework, called Learning beyond Classes (LBC), to train robust deep neural classifiers without requiring any annotations of spurious correlations in the data. The key components of the framework are: Automatic Spurious Correlation Detection: Leverages a pre-trained vision-language model to automatically detect attributes in the images. Proposes a spuriousness score to quantify the likelihood of a class-attribute correlation being spurious and exploited by the classifier. Spuriousness-Guided Training Data Relabeling: Constructs a spuriousness embedding space to characterize the classifier's prediction behaviors based on the detected attributes and their spuriousness scores. Clusters the training samples in the spuriousness embedding space and relabels them with fine-grained labels to diversify the classifier's outputs. Learning beyond Classes: Modifies the classifier architecture to predict the fine-grained labels instead of just class labels. Adopts within-class and cross-class balanced sampling strategies to address the imbalanced distribution of different prediction behaviors. The framework iteratively identifies and mitigates the classifier's reliance on spurious correlations, leading to improved robustness without any prior knowledge of the spurious attributes. Experiments on several real-world datasets demonstrate the effectiveness of the proposed method in outperforming state-of-the-art approaches.
The classifier trained with empirical risk minimization (ERM) can achieve high accuracy by exploiting spurious correlations between non-essential attributes and target classes. Obtaining annotations of spurious correlations typically requires expert knowledge and human supervision, which is a significant barrier in practice.
"Deep neural classifiers tend to rely on spurious cor-relations between spurious attributes of inputs and targets to make predictions, which could jeopar-dize their generalization capability." "Mitigating the reliance on spurious correlations is crucial for obtaining robust models."

Deeper Inquiries

How can the proposed self-guided framework be extended to handle more complex types of spurious correlations, such as those involving multiple attributes or higher-order interactions

The proposed self-guided framework can be extended to handle more complex types of spurious correlations by incorporating techniques to address multiple attributes or higher-order interactions. One approach could involve enhancing the spuriousness score calculation to consider interactions between multiple attributes and classes. This could involve developing a more sophisticated metric that captures the combined impact of multiple attributes on the classifier's predictions. Additionally, the spuriousness embedding space could be expanded to accommodate higher-order interactions by creating a multidimensional space that captures the relationships between various attributes and classes. By incorporating these enhancements, the framework can effectively address more intricate spurious correlations in the data.

What are the potential limitations of the vision-language model used for attribute detection, and how could the framework be adapted to handle cases where the detected attributes are not sufficiently informative or accurate

The vision-language model used for attribute detection may have limitations in terms of the accuracy and informativeness of the detected attributes. One potential limitation is the model's ability to accurately capture subtle or nuanced attributes that are crucial for identifying spurious correlations. To address this, the framework could be adapted by incorporating additional pre-processing steps or post-processing techniques to refine the detected attributes. For example, post-processing algorithms could filter out irrelevant or noisy attributes to improve the overall quality of the attribute set. Moreover, the framework could leverage ensemble methods or domain-specific knowledge to enhance the attribute detection process and ensure the detected attributes are more informative and accurate.

Can the ideas behind the spuriousness-guided training data relabeling and the learning beyond classes strategies be applied to other machine learning tasks beyond image classification, such as natural language processing or reinforcement learning

The ideas behind the spuriousness-guided training data relabeling and learning beyond classes strategies can be applied to various machine learning tasks beyond image classification. In natural language processing (NLP), the concept of spurious correlations can manifest in biased language models or text classifiers. By adapting the framework to NLP tasks, the spuriousness-guided relabeling approach can help mitigate biases in language models by identifying and addressing spurious correlations in textual data. Similarly, in reinforcement learning, where biases and spurious correlations can impact the learning process, the framework can be utilized to guide the training of agents to reduce reliance on misleading correlations and improve generalization to unseen scenarios. By applying these strategies across different machine learning domains, the framework can enhance the robustness and reliability of models in various applications.