Feedback-guided Synthetic Data Generation for Improving Imbalanced Classification

Concetti Chiave

Leveraging feedback from a pre-trained classifier to guide the generation of useful and diverse synthetic samples that are close to the real data distribution, in order to improve performance on imbalanced classification tasks.

Sintesi

The paper introduces a framework that leverages a pre-trained image generative model (Latent Diffusion Model) and a pre-trained classifier to generate synthetic samples that are useful for improving the classifier's performance on imbalanced classification tasks.

The key insights are:

1. Feedback from the classifier is essential to generate samples that are useful for improving the classifier's performance. The authors explore three feedback criteria: classifier loss, entropy, and hardness score.
2. To ensure the generated samples are close to the real data distribution, the authors use dual conditioning on both text prompts and randomly selected real images from the corresponding class.
3. To increase the diversity of the generated samples, the authors apply random dropout on the image embedding used for conditioning the generative model.

The authors validate the proposed framework on three imbalanced classification datasets: ImageNet-LT, Places-LT, and NICO++. They achieve state-of-the-art results, with significant improvements on underrepresented classes, while being more efficient in terms of the number of generated synthetic samples compared to prior work.

Statistiche

"We achieve state-of-the-art results on ImageNet-LT, with an improvement of 4% on underrepresented classes while using half the amount of synthetic data than the previous state-of-the-art."
"On Places-LT, we achieve state-of-the-art results as well as nearly 4% improvement on underrepresented classes."
"On NICO++, we achieve improvements of over 5% in worst group accuracy."

Citazioni

"Our framework paves the path towards effectively leveraging state-of-the-art text-to-image models as data sources that can be queried to improve downstream applications."
"We find that for the classifier's feedback to be effective, the synthetic data must lie close to the support of the downstream task data distribution, and be sufficiently diverse."

Approfondimenti chiave tratti da

Feedback-guided Data Synthesis for Imbalanced Classification

by Reyhane Aska... alle arxiv.org 09-11-2024

https://arxiv.org/pdf/2310.00158.pdf

Domande più approfondite

How can the proposed feedback-guided synthetic data generation framework be extended to other types of imbalanced datasets, such as those with long-tailed distributions in the feature space rather than the label space?

The proposed feedback-guided synthetic data generation framework can be adapted to address imbalanced datasets characterized by long-tailed distributions in the feature space by modifying the sampling strategy to focus on the feature characteristics rather than solely on class labels. This can be achieved through the following steps:

Feature Space Analysis: Begin by analyzing the feature distributions of the dataset to identify regions of the feature space that are underrepresented. This involves clustering techniques or density estimation methods to understand the distribution of features across different classes.

Feedback Mechanism: Extend the feedback mechanism to incorporate not just class labels but also feature representations. By utilizing a pre-trained model that captures the feature space dynamics, the framework can generate synthetic samples that are not only class-representative but also diverse in terms of feature characteristics.

Conditional Generation: Implement dual conditioning on both class labels and specific feature attributes. For instance, when generating synthetic samples, the framework can condition the generative model on a combination of class labels and specific feature vectors that represent the underrepresented regions in the feature space.

Diversity Enhancement: To ensure that the generated samples cover a broader range of the feature space, techniques such as random dropout on feature embeddings can be employed. This introduces variability in the generated samples, promoting diversity and reducing the risk of overfitting to specific feature patterns.

Evaluation Metrics: Adapt evaluation metrics to assess the performance of the model not only on classification accuracy but also on the coverage and density of the feature space. This ensures that the synthetic data generation process is effectively addressing the imbalances present in the feature space.

By implementing these strategies, the feedback-guided framework can effectively generate synthetic data that addresses imbalances in both the label and feature spaces, enhancing the robustness and generalization of downstream models.

What are the potential limitations and risks of using large-scale generative models as data sources for training downstream models, and how can these be mitigated?

Using large-scale generative models as data sources for training downstream models presents several limitations and risks, including:

Quality of Generated Data: The synthetic data generated may not always accurately represent the real data distribution, leading to a quality gap. This can result in models that perform poorly on real-world data. To mitigate this, rigorous validation of the generated samples against real data should be conducted, employing metrics such as Fréchet Inception Distance (FID) to assess the quality and diversity of the generated samples.

Bias and Ethical Concerns: Generative models can inadvertently perpetuate biases present in the training data, leading to biased synthetic samples. This can have ethical implications, especially in sensitive applications. To address this, it is crucial to implement bias detection and correction mechanisms during the training of generative models, ensuring that the generated data is representative and fair.

Overfitting to Synthetic Data: There is a risk that downstream models may overfit to the synthetic data, especially if the synthetic samples dominate the training process. To mitigate this, a balanced training approach should be adopted, where real and synthetic data are mixed in a controlled manner, ensuring that the model learns from both sources effectively.

Lack of Generalization: Models trained predominantly on synthetic data may struggle to generalize to real-world scenarios. To counter this, it is essential to maintain a strong baseline of real data in the training process and to continuously evaluate model performance on real-world datasets.

Computational Costs: Large-scale generative models can be computationally expensive to train and deploy. To mitigate this, leveraging pre-trained models and optimizing the sampling process can reduce computational overhead while still benefiting from the generative capabilities.

By addressing these limitations through careful validation, bias mitigation, balanced training, and computational optimization, the risks associated with using large-scale generative models can be significantly reduced, leading to more reliable and effective downstream models.

How can the proposed framework be combined with other algorithmic approaches for imbalanced classification, such as loss re-weighting or group-based optimization, to further improve performance?

The proposed feedback-guided synthetic data generation framework can be effectively combined with other algorithmic approaches for imbalanced classification to enhance performance through the following strategies:

Loss Re-weighting: Integrate loss re-weighting techniques with the feedback-guided framework to adjust the importance of different classes during training. By assigning higher weights to underrepresented classes, the model can focus more on learning from these classes, thereby improving overall classification performance. This can be particularly effective when combined with synthetic data that is generated to specifically target these underrepresented classes.

Group-Based Optimization: Implement group-based optimization strategies alongside the synthetic data generation process. This involves grouping classes based on their representation in the dataset and applying tailored optimization techniques to each group. For instance, the framework can generate synthetic samples for groups that are underrepresented and then apply group-specific loss functions or optimization algorithms to ensure that the model learns effectively from these groups.

Ensemble Methods: Combine the feedback-guided framework with ensemble learning techniques. By training multiple models on different subsets of the data (including both real and synthetic samples), the ensemble can leverage the strengths of each model, leading to improved robustness and accuracy. The feedback mechanism can be used to guide the generation of synthetic data that complements the weaknesses of individual models in the ensemble.

Active Learning: Incorporate active learning strategies where the model iteratively selects the most informative samples (both real and synthetic) for training. The feedback-guided framework can be used to generate synthetic samples that are specifically designed to challenge the model, thus enhancing the learning process. This approach ensures that the model continuously adapts to the most relevant data points.

Hybrid Approaches: Develop hybrid approaches that combine the feedback-guided framework with traditional data augmentation techniques. By augmenting real data with synthetic samples generated through the feedback mechanism, the model can benefit from a richer and more diverse training set, leading to improved generalization and performance on imbalanced datasets.

By leveraging these strategies, the proposed feedback-guided synthetic data generation framework can be effectively integrated with existing algorithmic approaches, resulting in a more comprehensive solution for tackling imbalanced classification challenges.