Improving Zero-Shot Classification by Adjusting Pretrained Model Predictions via Optimal Transport

Core Concepts

Pretrained models suffer from label distribution mismatch between pretraining and downstream tasks, which can be addressed by adjusting predictions via optimal transport.

Abstract

The content discusses a method called OTTER (Optimal TransporT adaptER) to improve zero-shot classification performance by adjusting pretrained model predictions to match the estimated label distribution of the downstream task.
Key highlights:

Zero-shot models inherit biases from their large pretraining datasets, particularly in the label distribution, which can significantly degrade performance on downstream tasks with different label distributions.
Existing approaches to address label distribution mismatch, such as fine-tuning or label shift adaptation, require access to labeled downstream data or knowledge of the true pretraining label distribution, which is often unavailable in zero-shot settings.
OTTER sidesteps these challenges by using optimal transport to rebalance the pretrained model's predictions based only on an estimate of the downstream label distribution.
Theoretically, the authors show that OTTER can recover the Bayes-optimal classifier under mild conditions and provide error bounds for the case of noisy label distribution estimates and prediction scores.
Empirically, the authors validate OTTER on a wide range of image and text zero-shot classification tasks, demonstrating significant accuracy improvements over baselines, especially in class-imbalanced datasets.
The authors also explore combining OTTER with few-shot learning techniques and leveraging class hierarchy information to further enhance zero-shot performance.

Stats

The content does not provide specific numerical data or statistics. It focuses on describing the proposed OTTER method and providing theoretical and empirical analysis.

Quotes

"Popular zero-shot models suffer due to artifacts inherited from pretraining. A particularly detrimental artifact, caused by unbalanced web-scale pretraining data, is mismatched label distribution."
"Can we deal with label distribution mismatch without additional training or access to ground-truth downstream task information? One cause for optimism is the observation that zero-shot models still give relatively high prediction probabilities for correct classes, though classes common in pretraining tend to have relatively inflated scores overall."
"Theoretically, we show that optimal transport given the true label distribution of the downstream can recover the Bayes-optimal classifier under mild conditions. Additionally, we provide error bounds on our adaptation method for misspecification."

Key Insights Distilled From

OTTER: Improving Zero-Shot Classification via Optimal Transport

by Changho Shin... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08461.pdf

Deeper Inquiries

How can the proposed OTTER method be extended to handle more complex distribution shifts beyond label distribution mismatch, such as feature distribution shifts or covariate shift

The OTTER method can be extended to handle more complex distribution shifts beyond label distribution mismatch by incorporating additional constraints and considerations into the optimal transport framework. One way to address feature distribution shifts is to include features as part of the transport plan, allowing for the alignment of feature distributions between the source and target domains. This can be achieved by modifying the cost matrix to incorporate both label and feature information, enabling the transport of data points based on both label and feature similarities.
For covariate shift, where the marginal distributions of the input features differ between the source and target domains, OTTER can be adapted to explicitly model and adjust for these differences. By incorporating covariate shift detection techniques into the optimal transport framework, such as domain adaptation methods or domain-invariant representations, OTTER can effectively handle shifts in the input feature distributions. This would involve modifying the transport plan to account for differences in the input feature distributions and adjusting the predictions accordingly.
Overall, by integrating feature alignment and covariate shift detection mechanisms into the OTTER method, it can be extended to address more complex distribution shifts beyond label distribution mismatch, enhancing its applicability to a wider range of domain adaptation and transfer learning scenarios.

What are the potential limitations or drawbacks of the optimal transport-based approach, and how could they be addressed in future work

While the optimal transport-based approach, such as OTTER, offers a promising solution for handling label distribution mismatch in zero-shot classification, there are potential limitations and drawbacks that should be considered for future work:

Computational Complexity: Optimal transport can be computationally intensive, especially for large datasets or high-dimensional feature spaces. Addressing this limitation may involve developing more efficient algorithms or approximations to speed up the optimization process.

Sensitivity to Noise: OTTER's performance may be sensitive to noise in the label distribution specification or prediction scores. Robustness to noisy or inaccurate estimates of the label distribution could be improved through the incorporation of uncertainty estimates or regularization techniques.

Scalability: Scaling OTTER to handle large-scale datasets or complex distribution shifts may pose challenges. Future research could focus on developing scalable implementations or parallelization strategies to enhance the method's scalability.

Interpretability: The interpretability of the optimal transport-based adjustments made by OTTER may be limited, making it challenging to understand the reasoning behind the model's predictions. Exploring methods to enhance the interpretability of the transport plan adjustments could be beneficial.

Addressing these limitations through algorithmic improvements, robustness enhancements, scalability optimizations, and interpretability enhancements can further advance the effectiveness and applicability of the optimal transport-based approach in handling distribution shifts.

Given the success of OTTER in zero-shot and few-shot settings, how could the insights from this work be applied to improve the robustness and generalization of large pretrained models in general

The insights from the success of OTTER in zero-shot and few-shot settings can be applied to improve the robustness and generalization of large pretrained models in general by:

Domain Adaptation: Leveraging the principles of optimal transport, pretrained models can be adapted to new domains or tasks by adjusting their predictions based on the distributional differences between the pretraining data and the target data. This can help improve the model's performance on unseen or shifted data distributions.

Bias Correction: OTTER's ability to address label distribution mismatch can be utilized to mitigate biases present in pretrained models. By adjusting the model's predictions to align with the true label distribution of the target task, biases inherited from the pretraining data can be reduced, leading to more fair and accurate predictions.

Transfer Learning: The concept of adjusting model predictions based on distributional differences can be applied in transfer learning scenarios to enhance the transferability of pretrained models across different tasks or domains. By fine-tuning the model's predictions using optimal transport principles, the model can adapt more effectively to new data distributions.

By incorporating the insights and methodologies from OTTER into the training and adaptation processes of large pretrained models, it is possible to enhance their robustness, generalization capabilities, and performance on diverse and challenging datasets.