Visual Data Diagnosis and Debiasing with Concept Graphs

Summary

The paper introduces a method for addressing the issue of inherent bias in data during the training process. This bias can lead to unreliable predictions from the model. The proposed method, called ConBias, is a new approach for identifying and mitigating Concept co-occurrence Biases in visual datasets. The method represents visual datasets as knowledge graphs of concepts, allowing for detailed analysis of misleading concept co-occurrences that reveal imbalances across the dataset. Additionally, the author demonstrates how a new clique-based concept balancing strategy can address these imbalances, resulting in improved performance on subsequent tasks. Extensive experiments show that applying data augmentation based on the proposed method leads to significant improvements in generalization performance across various datasets when compared to state-of-the-art methods.

Strengths

The paper is well-written and explains the method thoroughly. The authors' experiments on various common datasets demonstrate the method's effectiveness and the comparisons to a prior art show that it achieves state-of-the-art performance. The paper includes a thorough analysis of the method's components, justifying its contributions.

Weaknesses

In the paper, I missed the ablation study of the authors' knowledge graphs design, the impact of using a different number of graph layers, knowledge graph architectures, different initializations, etc.

Questions

https://arxiv.org/abs/2310.04562 proposes "ULTRA" KD foundation graph pre-training. Can using this method benefit the proposed graph structure initialization?

Rating

6

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

The authors have properly addressed the proposed method limitations.

Summary

The authors present ConBias, which is a novel approach for diagnosing and de-biasing co-occurrence bias in datasets. Unlike previous works, ConBias addresses both diagnosis and de-biasing / balancing strategy, to target the data augmentation specifically to address spurious concept co-occurrences. The main components of ConBias are as follows: 1. New concept graph-based analysis framework which involves occurrences of common concepts and classes in the data in a graph-form to identify spurious correlations 2. Identify main cliques that occur across all classes, and determine the frequency of occurrence with respect to each classes to find spuriously correlated concepts 3. re-balance the dataset based on the imbalanced concept cliques. specifically, generate new backgrounds for the objects using an inpainting-based augmentation. Then they perform a thorough evaluation of their new re-balanced dataset across various evaluation protocols including the class-balanced test data and the OOD test data, as well as other metrics for shortcut learning. The results show that ConBias consistently outperforms other existing baselines. The authors also show ablations to justify their design decisions around the graph structure, as well as the generative model choice.

Strengths

Originality: The work demonstrates originality by introducing targeted dataset-debiasing based on concept co-occurrence diagnosis within the dataset augmentation space. Unlike previous approaches that often debias without diagnosing or only diagnose biases within datasets without suggesting effective debiasing methods directly to the data, this method focuses on improving the robustness of binary classification tasks through meticulous dataset examination. Quality: The paper presents a novel approach that consistently outperforms existing debiasing augmentation methods. The experimental setup spans multiple datasets across various domains, providing robust justification for the proposed approach. Results are clearly presented, demonstrating reasonable improvements in performance metrics. Clarity: The paper is well-written and logically organized, making it easy to follow the ConBias pipeline. Graphs and illustrations are informative, enhancing understanding of each step and experimental setup. Significance: This work introduces a debiasing method aimed at reducing overfitting to spurious correlations. This is significant as it can improve the reliability of vision tasks across various applications, especially in domains where robust performance on out-of-distribution (OOD) cases is crucial, such as healthcare diagnosis

Weaknesses

The binary classification test bench used in the experiments may be insufficient in scope to fully demonstrate the potential of the proposed method. While the results show promise, it remains unclear if the method's performance scales effectively to multiple sets of classes, which is crucial for assessing its broader impact and significance. Moreover, in scenarios with multiple sets of classes, there exist spurious correlations across different subsets that require a more sophisticated and targeted sampling strategy than what the current approach, which extracts shared cliques across all classes, provides. In addition, I believe further justification or benchmarking against debiasing methods in the feature space is necessary. Although this work pioneers combined diagnosis and debiasing in the dataset space, previous research has focused on diagnosing and debiasing models at training time or in feature spaces. Earlier methods required dataset bias labels, while recent advancements explore shortcut learning without explicit labels. The paper briefly mentions choosing the dataset space for enhanced interpretability but does not fully justify the computational trade-offs involved in generating balanced data as well as concept labels. The work would benefit significantly from additional benchmarking against state-of-the-art feature space analysis and debiasing methods where they are able to demonstrate improvements or providing a detailed rationale for its focus on the dataset debiasing.

Questions

Related to the preceding section, I have several inquiries for the authors: 1. Could you provide insights into how this method might scale effectively in a multi-class classification scenario? What are your expectations regarding its performance scalability? 2. Could you clarify why additional evaluation metrics were exclusively presented for UrbanCars and not for Waterbirds or Coco-GB datasets? 3. How precisely is the upsampling conducted concerning different sets of cliques? While I understand the additional image count aligns with ALIA's methodology for fair comparison, it remains unclear how many images this comprises, how various co-occurring object sets contribute to the augmentation dataset distribution, and the criteria used in making these decisions.

Rating

5

Confidence

3

Soundness

3

Presentation

4

Contribution

2

Limitations

Yes, the authors sufficiently address limitations and societal impact in the final section of their paper.

Summary

This paper points out inherent biases in visual dataset. To address this issue, the paper proposes a new framework called ConBias, which proceeds in three steps: (1) Concept Graph Construction, (2) Concept Diagnosis, and (3) Concept Debiasing. Using concept metadata in the dataset, concept graph is constructed. After identifying imbalanced clique sets, ConBias utilizes Stable Diffusion to generate images containing under-represented concepts across classes. The experimental results show the effectiveness of the proposed framework to mitigate dataset biases.

Strengths

1. The paper proposes a new concept graph-based framework, which is easy to construct using metadata and diagnose biases in visual datasets. 2. A new approach to generate unbiased images using stable diffusion robustly enhances the overall performance. 3. The experimental results across different datasets show reasonable performances compared to the state-of-the-art method.

Weaknesses

1. The main concern is that the proposed framework should require metadata to construct a graph. It can be a problem to apply this method directly in many cases where visual datasets do not include any metadata. 2. I do not agree that the existing approach utilizing LLMs is no reliable way, since (1) there is also a possibility that the given metadata has wrong or biased information, and (2) as recent LLMs become powerful, the quality of domain descriptions is reliable (rather automatically improving the quality as time goes by). To sum up, there is no guarantee that the proposed method is always less biased compared to the existing method. 3. The processes to construct graph and diagnose bias do not look technically novel. The overall process is primarily based on a counting mechanism, not a learning-based method. 4. The authors tested the proposed framework on three benchmark datasets. However, the used datasets look like toy experiments, i.e., all the tasks are binary classification tasks and easy to identify biases. I wonder that the proposed method still constructs and detects inherent, complex biases in large and multi-class tasks, e.g., ImageNet. 5. The experimental results in the tables are quite different from the values displayed in the ALIA paper [8]. If it is true, can you explain the reasons? 6. There are no comparisons about computational complexity between the existing methods and the proposed one. Please clarify how the costs are changed per the number of total concepts.

Questions

1. Please describe total numbers of generated graph nodes and edges.

Rating

4

Confidence

4

Soundness

2

Presentation

3

Contribution

2

Limitations

The authors address limitations and broader impact well in the paper.

Summary

This paper introduces a concept graph-based framework to diagnose and mitigate biases in visual datasets by representing datasets as knowledge graphs of object co-occurrences. The approach involves constructing a concept graph, diagnosing concept imbalances, and debiasing by generating images with under-represented concept combinations. This method enhances dataset balance and improves generalization performance across multiple datasets.

Strengths

 The paper presents a structured and controllable method for diagnosing and mitigating spurious correlations by representing datasets as knowledge graphs of object co-occurrences.  Experimental results demonstrate that balanced concept generation in data augmentation enhances classifier generalization across multiple datasets, outperforming baseline methods.  This manuscript is written well, it’s easy to read and follow.

Weaknesses

This manuscript seems to be an engineering technical report about the data augment and the technical contribution is weak for an academic paper. The method of constructing a knowledge graph to determine object co-occurrences is overly direct; statistical analysis of objects and frequency comparison can achieve similar results. • What is the advanced intuition behind constructing a knowledge graph to determine object co-occurrences? How does it compare to previous methods in terms of advantages? • The paper lacks details on the concept set annotations. How might the quality of these annotations affect the performance of the proposed method? • Why are the results of ALIA on Table 1 difference with ALIA[1]. [1] Lisa Dunlap, Alyssa Umino, Han Zhang, Jiezhi Yang, Joseph E Gonzalez, and Trevor Darrell. Diversify your vision datasets with automatic diffusion-based augmentation. Advances in Neural Information Processing Systems, 36, 2024 There is a typographical error on line 198: a space is missing after the period.

Questions

see above

Rating

5

Confidence

3

Soundness

2

Presentation

3

Contribution

2

Limitations

Yes, the authors discuss about limitations in section 5.