Estimating a Model's Performance on Unseen Domains at Test Time using Optimal Transport

Core Concepts

A metric based on Optimal Transport can efficiently estimate a model's performance on unseen domains at test time using only unlabeled data from the target domain and information about the source domain.

Abstract

The paper proposes a metric called TETOT (Test-time Estimation of Transferability via Optimal Transport) to assess a model's performance on unseen domains at test time. The key insights are: The performance of a model on in-distribution data is a poor indicator of its performance on data from unseen domains. Thus, it is essential to develop metrics that can provide insights into the model's performance at test time. TETOT computes the distributional divergence between the source and target domains using Optimal Transport. It uses both feature-level and label-level information to compute this divergence. TETOT can be efficiently computed at test time using only unlabeled data from the target domain, the model parameters, and information about the source domain (data or statistics). The authors extensively evaluate TETOT on standard benchmark datasets (PACS and VLCS) and their corrupted versions. They demonstrate that TETOT achieves a significantly higher correlation with the ground truth transferability compared to the popular prediction entropy-based metric. The authors show the utility of TETOT in various practical applications, including architecture selection, source dataset selection, and estimating a model's performance on unseen domains at test time. They also propose a variant of TETOT that can be computed using only the statistics of the source domain, which is useful when the source data is not accessible.

Stats

The model's accuracy on the target domain is used as the ground truth transferability. TETOT uses a small number of labeled samples from the source domain and unlabeled samples from the target domain to compute the distributional divergence. The authors evaluate TETOT on standard benchmark datasets (PACS and VLCS) and their corrupted versions.

Quotes

"Gauging the performance of ML models on data from unseen domains at test-time is essential yet a challenging problem due to the lack of labels in this setting." "TETOT characterizes the model's performance on unseen domains using only a small amount of unlabeled data from these domains and data or statistics from the training (source) domain(s)." "Our empirical results show that our metric, which uses information from both the source and the unseen domain, is highly correlated with the model's performance, achieving a significantly better correlation than that obtained via the popular prediction entropy-based metric, which is computed solely using the data from the unseen domain."

Key Insights Distilled From

Test-time Assessment of a Model's Performance on Unseen Domains via Optimal Transport

by Akshay Mehra... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01451.pdf

Deeper Inquiries

How can TETOT be extended to handle multiple source domains with different label spaces

To extend TETOT to handle multiple source domains with different label spaces, we can modify the label cost calculation in the base distance function (Eq. 4). When dealing with multiple source domains with varying label spaces, we need to consider the alignment of labels across these domains. One approach is to map the labels from different source domains to a common label space before computing the label cost. This mapping can be achieved through techniques like label embedding or label alignment methods. By aligning the labels from different domains to a common space, we can ensure that the label cost in TETOT reflects the similarity between the predicted labels and the ground truth labels across all the source domains.

How can TETOT be used to guide the model architecture and training process to improve transferability to unseen domains

TETOT can be used to guide the model architecture and training process to improve transferability to unseen domains by serving as a metric for model selection and hyperparameter tuning. Here are some key steps to leverage TETOT for enhancing transferability: Model Architecture Selection: Use TETOT to compare the transferability of different model architectures on unseen domains. Select the architecture that shows the highest correlation with transferability as estimated by TETOT. Hyperparameter Tuning: Adjust hyperparameters such as learning rate, regularization strength, and batch size based on the TETOT scores. Optimize these parameters to maximize transferability to unseen domains. Data Augmentation Strategies: Incorporate data augmentation techniques that minimize the distributional divergence between the source and target domains, as indicated by TETOT. This can help the model generalize better to unseen data. Domain Adaptation Techniques: Implement domain adaptation methods that align the feature distributions of source and target domains based on insights from TETOT. This alignment can improve the model's performance on unseen domains. By integrating TETOT into the model development pipeline, practitioners can make informed decisions to enhance the model's transferability to unseen domains.

What are the potential applications of TETOT in real-world scenarios where the source data may be private or costly to access

The potential applications of TETOT in real-world scenarios where the source data may be private or costly to access are diverse and impactful. Some of these applications include: Privacy-Preserving Model Evaluation: TETOT can be used to assess the transferability of models without requiring access to the actual source data. This is particularly useful in scenarios where the source data contains sensitive information that cannot be shared. Cost-Effective Model Selection: In situations where acquiring source data is expensive or resource-intensive, TETOT can guide the selection of the best model architecture and training process for optimal performance on unseen domains, without the need to access the source data directly. Secure Model Deployment: TETOT can help in evaluating the robustness of models to distribution shifts without compromising the confidentiality of the source data. This ensures that deployed models perform well on unseen data while maintaining data privacy. Cross-Domain Knowledge Transfer: By leveraging TETOT, organizations can transfer knowledge and insights gained from one domain to another without sharing the actual data. This facilitates collaboration and learning across diverse domains while protecting data privacy. Overall, TETOT offers a versatile and practical solution for estimating model performance on unseen domains in scenarios where accessing the source data is restricted or costly.